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      name:  <unnamed>
       log:  C:\Users\LIM58\Dropbox\Ginecologia e Obstetrícia\Isabel - Atenção primária a saúde\atend.log
  log type:  text
 opened on:  14 Jun 2021, 11:24:18

. label variable regiao "Região de moradia"

. label variable data "Data do atendimento"

. label variable ano "Ano do atendimento"

. label variable prepos "Pré - Pós COVID-19"

. label variable iniciovs "Idade inicio VSA"

. label variable cidgeral "CID Geral"

. label variable cid1 "CID 1"

. label variable cid2 "CID 2"

. label variable cid3 "CID 3"

. label variable cid4 "CID 4"

. label variable cid5 "CID 5"

. label variable gesta "Número de gestações"

. label variable partovag "Número de partos vaginais"

. label variable partocesar "Número de partos cesárea"

. label variable forceps "Número de partos Fórceps"

. label variable aborto "Número de abortos"

. label variable vidasexual "Vida sexual ativa / inativa"

. label variable menarca "Idade menarca"

. label variable coitarca "Idade coitarca"

. label variable hipo "Hipotireoidismo"

. label variable endometr "Endometriose"

. label variable obesidade "Obesidade"

. label variable has "Hipertensão arterial"

. label variable dm "DMT2"

. label variable dlp "Dislipidemia"

. label variable fogacho "Fogacho"

. label variable sangr "Sangramento"

. label variable diu "DIU"

. label variable osteop "Osteoporose"

. label variable cardiop "Cardiopatia"

. label variable menop "Menopausa"

. label variable idademenopausa "Idade da menopausa"

. label variable peso "Peso"

. label variable altura "Altura"

. label variable imc "IMC"

. label variable pas "PAS"

. label variable pad "PAD"

. label define prepos 0"Pré COVID-19" 1"Pós COVID-19"

. label val prepos prepos

. tab prepos

   Pré - Pós |
    COVID-19 |      Freq.     Percent        Cum.
-------------+-----------------------------------
Pré COVID-19 |        399       67.86       67.86
Pós COVID-19 |        189       32.14      100.00
-------------+-----------------------------------
       Total |        588      100.00

. tab ano

     Ano do |
atendimento |      Freq.     Percent        Cum.
------------+-----------------------------------
       2019 |        349       59.35       59.35
       2020 |        191       32.48       91.84
       2021 |         48        8.16      100.00
------------+-----------------------------------
      Total |        588      100.00

. label define simnao 0"Não" 1"Sim"

. label val simnao hipo endometr obesidade has dm dlp fogacho sangr diu osteop cardiop
variable simnao not found
r(111);

. label val hipo endometr obesidade has dm dlp fogacho sangr diu osteop cardiop simnao

. tab cardiop

Cardiopatia |      Freq.     Percent        Cum.
------------+-----------------------------------
        Não |        576       97.96       97.96
        Sim |         12        2.04      100.00
------------+-----------------------------------
      Total |        588      100.00

. label val menop simnao

. tab menop

  Menopausa |      Freq.     Percent        Cum.
------------+-----------------------------------
        Não |        305       51.87       51.87
        Sim |        283       48.13      100.00
------------+-----------------------------------
      Total |        588      100.00

. label define regiao 0"São Paulo" 1"Outros"

. tab regiao

  Região de |
    moradia |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        248       47.97       47.97
          1 |        269       52.03      100.00
------------+-----------------------------------
      Total |        517      100.00

. label val regiao regiao

. tab regiao

  Região de |
    moradia |      Freq.     Percent        Cum.
------------+-----------------------------------
  São Paulo |        248       47.97       47.97
     Outros |        269       52.03      100.00
------------+-----------------------------------
      Total |        517      100.00

. sort vidasexual

. tab vidasexual

Vida sexual |
    ativa / |
    inativa |      Freq.     Percent        Cum.
------------+-----------------------------------
      ativa |        186       61.39       61.39
    inativa |        117       38.61      100.00
------------+-----------------------------------
      Total |        303      100.00

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(1 real change made)

. replace vidasexual = "1" in 294
(1 real change made)

. replace vidasexual = "1" in 293
(1 real change made)

. replace vidasexual = "1" in 292
(1 real change made)

. replace vidasexual = "1" in 291
(1 real change made)

. replace vidasexual = "1" in 290
(1 real change made)

. replace vidasexual = "1" in 289
(1 real change made)

. replace vidasexual = "1" in 288
(1 real change made)

. replace vidasexual = "1" in 285
(1 real change made)

. replace vidasexual = "1" in 284
(1 real change made)

. tab vidasexual

Vida sexual |
    ativa / |
    inativa |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        122       39.35       39.35
          1 |        188       60.65      100.00
------------+-----------------------------------
      Total |        310      100.00

. label define vida 0"Inativa" 1"Ativa"

. label val vidasexual vida
may not label strings
r(181);

. destring vidasexual, replace
vidasexual: all characters numeric; replaced as byte
(283 missing values generated)

. destring vidasexual, replace
vidasexual already numeric; no replace

. label val vidasexual vida

. tab vidasexual

Vida sexual |
    ativa / |
    inativa |      Freq.     Percent        Cum.
------------+-----------------------------------
    Inativa |        122       39.35       39.35
      Ativa |        188       60.65      100.00
------------+-----------------------------------
      Total |        310      100.00

. tab cor

        cor |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        441       75.00       75.00
          1 |         10        1.70       76.70
          2 |         41        6.97       83.67
          3 |         51        8.67       92.35
          4 |         45        7.65      100.00
------------+-----------------------------------
      Total |        588      100.00

. label define cor 0"Branca" 1"Amarela" 2"Parda" 3"Negra" 4"Ignorado"

. label val cor cor

. tab cor

        cor |      Freq.     Percent        Cum.
------------+-----------------------------------
     Branca |        441       75.00       75.00
    Amarela |         10        1.70       76.70
      Parda |         41        6.97       83.67
      Negra |         51        8.67       92.35
   Ignorado |         45        7.65      100.00
------------+-----------------------------------
      Total |        588      100.00

. sort rghc

. bysort prepos: tab cid1

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pré COVID-19

      CID 1 |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |         24       96.00       96.00
          2 |          1        4.00      100.00
------------+-----------------------------------
      Total |         25      100.00

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pós COVID-19

      CID 1 |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |         13      100.00      100.00
------------+-----------------------------------
      Total |         13      100.00

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = .
no observations


. bysort prepos:tab cid2

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pré COVID-19

      CID 2 |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |         10      100.00      100.00
------------+-----------------------------------
      Total |         10      100.00

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pós COVID-19

      CID 2 |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |          7      100.00      100.00
------------+-----------------------------------
      Total |          7      100.00

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = .
no observations


. bysort prepos: tab cid3

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pré COVID-19

      CID 3 |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |          6      100.00      100.00
------------+-----------------------------------
      Total |          6      100.00

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pós COVID-19

      CID 3 |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |          7      100.00      100.00
------------+-----------------------------------
      Total |          7      100.00

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = .
no observations


. bysort prepos:tab cid4

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pré COVID-19

      CID 4 |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        303       93.81       93.81
          2 |         18        5.57       99.38
          3 |          2        0.62      100.00
------------+-----------------------------------
      Total |        323      100.00

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pós COVID-19

      CID 4 |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        145       91.77       91.77
          2 |         11        6.96       98.73
          3 |          2        1.27      100.00
------------+-----------------------------------
      Total |        158      100.00

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = .
no observations


. bysort prepos: tab cid5

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pré COVID-19

      CID 5 |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |         67      100.00      100.00
------------+-----------------------------------
      Total |         67      100.00

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pós COVID-19

      CID 5 |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |         17      100.00      100.00
------------+-----------------------------------
      Total |         17      100.00

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = .
no observations


. replace pad = . in 589
(0 real changes made)

.  bysort ano: tab cid4

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> ano = 2019

      CID 4 |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        263       93.59       93.59
          2 |         16        5.69       99.29
          3 |          2        0.71      100.00
------------+-----------------------------------
      Total |        281      100.00

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> ano = 2020

      CID 4 |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        144       91.14       91.14
          2 |         12        7.59       98.73
          3 |          2        1.27      100.00
------------+-----------------------------------
      Total |        158      100.00

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> ano = 2021

      CID 4 |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |         41       97.62       97.62
          2 |          1        2.38      100.00
------------+-----------------------------------
      Total |         42      100.00

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> ano = .
no observations


. replace idade = . in 589
(0 real changes made)

. replace vidasexual = . in 589
(1 real change made, 1 to missing)

. replace menarca = . in 590
(0 real changes made)

. replace menarca = . in 590
(0 real changes made)

. replace vidasexual = . in 590
(1 real change made, 1 to missing)

. replace vidasexual = . in 591
(1 real change made, 1 to missing)

. replace vidasexual = . in 592
(1 real change made, 1 to missing)

. replace vidasexual = . in 593
(1 real change made, 1 to missing)

. sort rghc

. drop _all

. *(44 variables, 588 observations pasted into data editor)

. do "C:\Users\LIM58\AppData\Local\Temp\STD00000000.tmp"

. label variable regiao "Região de moradia"

. 
end of do-file

. do "C:\Users\LIM58\AppData\Local\Temp\STD00000000.tmp"

. label variable regiao "Região de moradia"

. label variable data "Data do atendimento"

. label variable ano "Ano do atendimento"

. label variable prepos "Pré - Pós COVID-19"

. label variable iniciovs "Idade inicio VSA"

. label variable cidgeral "CID Geral"

. label variable cid1 "CID 1"

. label variable cid2 "CID 2"

. label variable cid3 "CID 3"

. label variable cid4 "CID 4"

. label variable cid5 "CID 5"

. label variable gesta "Número de gestações"

. label variable partovag "Número de partos vaginais"

. label variable partocesar "Número de partos cesárea"

. label variable forceps "Número de partos Fórceps"

. label variable aborto "Número de abortos"

. label variable vidasexual "Vida sexual ativa / inativa"

. label variable menarca "Idade menarca"

. label variable coitarca "Idade coitarca"

. label variable hipo "Hipotireoidismo"

. label variable endometr "Endometriose"

. label variable obesidade "Obesidade"

. label variable has "Hipertensão arterial"

. label variable dm "DMT2"

. label variable dlp "Dislipidemia"

. label variable fogacho "Fogacho"

. label variable sangr "Sangramento"

. label variable diu "DIU"

. label variable osteop "Osteoporose"

. label variable cardiop "Cardiopatia"

. label variable menop "Menopausa"

. label variable idademenopausa "Idade da menopausa"

. label variable peso "Peso"

. label variable altura "Altura"

. label variable imc "IMC"

. label variable pas "PAS"

. label variable pad "PAD"

. label define prepos 0"Pré COVID-19" 1"Pós COVID-19"
label prepos already defined
r(110);

end of do-file

r(110);

. do "C:\Users\LIM58\AppData\Local\Temp\STD00000000.tmp"

. label variable iniciovs "Idade inicio VSA"

. label variable cidgeral "CID Geral"

. label variable cid1 "CID 1"

. label variable cid2 "CID 2"

. label variable cid3 "CID 3"

. label variable cid4 "CID 4"

. label variable cid5 "CID 5"

. label variable gesta "Número de gestações"

. label variable partovag "Número de partos vaginais"

. label variable partocesar "Número de partos cesárea"

. label variable forceps "Número de partos Fórceps"

. label variable aborto "Número de abortos"

. label variable vidasexual "Vida sexual ativa / inativa"

. label variable menarca "Idade menarca"

. label variable coitarca "Idade coitarca"

. label variable hipo "Hipotireoidismo"

. label variable endometr "Endometriose"

. label variable obesidade "Obesidade"

. label variable has "Hipertensão arterial"

. label variable dm "DMT2"

. label variable dlp "Dislipidemia"

. label variable fogacho "Fogacho"

. label variable sangr "Sangramento"

. label variable diu "DIU"

. label variable osteop "Osteoporose"

. label variable cardiop "Cardiopatia"

. label variable menop "Menopausa"

. label variable idademenopausa "Idade da menopausa"

. label variable peso "Peso"

. label variable altura "Altura"

. label variable imc "IMC"

. label variable pas "PAS"

. label variable pad "PAD"

. label define prepos 0"Pré COVID-19" 1"Pós COVID-19"
label prepos already defined
r(110);

end of do-file

r(110);

. do "C:\Users\LIM58\AppData\Local\Temp\STD00000000.tmp"

. label val prepos prepos

. label define simnao 0"Não" 1"Sim"
label simnao already defined
r(110);

end of do-file

r(110);

. do "C:\Users\LIM58\AppData\Local\Temp\STD00000000.tmp"

. label val hipo endometr obesidade has dm dlp fogacho sangr diu osteop cardiop simnao

. label val menop simnao

. label define regiao 0"São Paulo" 1"Outros"
label regiao already defined
r(110);

end of do-file

r(110);

. do "C:\Users\LIM58\AppData\Local\Temp\STD00000000.tmp"

. label val regiao regiao

. label define vida 0"Inativa" 1"Ativa"
label vida already defined
r(110);

end of do-file

r(110);

. do "C:\Users\LIM58\AppData\Local\Temp\STD00000000.tmp"

. label val vidasexual vida

. label define cor 0"Branca" 1"Amarela" 2"Parda" 3"Negra" 4"Ignorado"
label cor already defined
r(110);

end of do-file

r(110);

. label define cor 0"Branca" 1"Amarela" 2"Parda" 3"Negra" 4"Ignorado"
label cor already defined
r(110);

. bysort prepos:tab regiao

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pré COVID-19

  Região de |
    moradia |      Freq.     Percent        Cum.
------------+-----------------------------------
  São Paulo |        175       49.16       49.16
     Outros |        181       50.84      100.00
------------+-----------------------------------
      Total |        356      100.00

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pós COVID-19

  Região de |
    moradia |      Freq.     Percent        Cum.
------------+-----------------------------------
  São Paulo |         73       45.34       45.34
     Outros |         88       54.66      100.00
------------+-----------------------------------
      Total |        161      100.00


. tab mes

        mes |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |         26        4.42        4.42
          2 |         29        4.93        9.35
          3 |         18        3.06       12.41
          4 |         20        3.40       15.82
          5 |         29        4.93       20.75
          6 |         25        4.25       25.00
          7 |         45        7.65       32.65
          8 |         34        5.78       38.44
          9 |         34        5.78       44.22
         10 |         28        4.76       48.98
         11 |         29        4.93       53.91
         12 |         32        5.44       59.35
         13 |         32        5.44       64.80
         14 |         25        4.25       69.05
         15 |         15        2.55       71.60
         16 |          7        1.19       72.79
         18 |          2        0.34       73.13
         19 |         12        2.04       75.17
         20 |         26        4.42       79.59
         21 |         13        2.21       81.80
         22 |         21        3.57       85.37
         23 |         15        2.55       87.93
         24 |         23        3.91       91.84
         25 |          7        1.19       93.03
         26 |         14        2.38       95.41
         27 |         24        4.08       99.49
         28 |          3        0.51      100.00
------------+-----------------------------------
      Total |        588      100.00

. save "C:\Users\LIM58\Dropbox\Ginecologia e Obstetrícia\Isabel - Atenção primária a saúde\atendimentos.dta", replace
file C:\Users\LIM58\Dropbox\Ginecologia e Obstetrícia\Isabel - Atenção primária a saúde\atendimentos.dta saved

. exit, clear
----------------------------------------------------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  C:\Users\LIM58\Dropbox\Ginecologia e Obstetrícia\Isabel - Atenção primária a saúde\atend.log
  log type:  text
 opened on:  15 Jun 2021, 09:15:19

. drop _all

. *(45 variables, 588 observations pasted into data editor)

. do "C:\Users\LIM58\AppData\Local\Temp\STD00000000.tmp"

. label variable regiao "Região de moradia"

. label variable data "Data do atendimento"

. label variable ano "Ano do atendimento"

. label variable prepos "Pré - Pós COVID-19"

. label variable iniciovs "Idade inicio VSA"

. label variable cidgeral "CID Geral"

. label variable cid1 "CID 1"

. label variable cid2 "CID 2"

. label variable cid3 "CID 3"

. label variable cid4 "CID 4"

. label variable cid5 "CID 5"

. label variable gesta "Número de gestações"

. label variable partovag "Número de partos vaginais"

. label variable partocesar "Número de partos cesárea"

. label variable forceps "Número de partos Fórceps"

. label variable aborto "Número de abortos"

. label variable vidasexual "Vida sexual ativa / inativa"

. label variable menarca "Idade menarca"

. label variable coitarca "Idade coitarca"

. label variable hipo "Hipotireoidismo"

. label variable endometr "Endometriose"

. label variable obesidade "Obesidade"

. label variable has "Hipertensão arterial"

. label variable dm "DMT2"

. label variable dlp "Dislipidemia"

. label variable fogacho "Fogacho"

. label variable sangr "Sangramento"

. label variable diu "DIU"

. label variable osteop "Osteoporose"

. label variable cardiop "Cardiopatia"

. label variable menop "Menopausa"

. label variable idademenopausa "Idade da menopausa"

. label variable peso "Peso"

. label variable altura "Altura"

. label variable imc "IMC"

. label variable pas "PAS"

. label variable pad "PAD"

. label define prepos 0"Pré COVID-19" 1"Pós COVID-19"
label prepos already defined
r(110);

end of do-file

r(110);

. do "C:\Users\LIM58\AppData\Local\Temp\STD00000000.tmp"

. label variable regiao "Região de moradia"

. label variable data "Data do atendimento"

. label variable ano "Ano do atendimento"

. label variable prepos "Pré - Pós COVID-19"

. label variable iniciovs "Idade inicio VSA"

. label variable cidgeral "CID Geral"

. label variable cid1 "CID 1"

. label variable cid2 "CID 2"

. label variable cid3 "CID 3"

. label variable cid4 "CID 4"

. label variable cid5 "CID 5"

. label variable gesta "Número de gestações"

. label variable partovag "Número de partos vaginais"

. label variable partocesar "Número de partos cesárea"

. label variable forceps "Número de partos Fórceps"

. label variable aborto "Número de abortos"

. label variable vidasexual "Vida sexual ativa / inativa"

. label variable menarca "Idade menarca"

. label variable coitarca "Idade coitarca"

. label variable hipo "Hipotireoidismo"

. label variable endometr "Endometriose"

. label variable obesidade "Obesidade"

. label variable has "Hipertensão arterial"

. label variable dm "DMT2"

. label variable dlp "Dislipidemia"

. label variable fogacho "Fogacho"

. label variable sangr "Sangramento"

. label variable diu "DIU"

. label variable osteop "Osteoporose"

. label variable cardiop "Cardiopatia"

. label variable menop "Menopausa"

. label variable idademenopausa "Idade da menopausa"

. label variable peso "Peso"

. label variable altura "Altura"

. label variable imc "IMC"

. label variable pas "PAS"

. label variable pad "PAD"

. label define prepos 0"Pré COVID-19" 1"Pós COVID-19"
label prepos already defined
r(110);

end of do-file

r(110);

. do "C:\Users\LIM58\AppData\Local\Temp\STD00000000.tmp"

. label define prepos 0"Pré COVID-19" 1"Pós COVID-19"
label prepos already defined
r(110);

end of do-file

r(110);

. do "C:\Users\LIM58\AppData\Local\Temp\STD00000000.tmp"

. label val prepos prepos

. 
end of do-file

. do "C:\Users\LIM58\AppData\Local\Temp\STD00000000.tmp"

. label val hipo endometr obesidade has dm dlp fogacho sangr diu osteop cardiop simnao

. 
end of do-file

. do "C:\Users\LIM58\AppData\Local\Temp\STD00000000.tmp"

. label val menop simnao

. 
end of do-file

. do "C:\Users\LIM58\AppData\Local\Temp\STD00000000.tmp"

. label val regiao regiao

. 
end of do-file

. do "C:\Users\LIM58\AppData\Local\Temp\STD00000000.tmp"

. label val vidasexual vida

. 
end of do-file

. do "C:\Users\LIM58\AppData\Local\Temp\STD00000000.tmp"

. label define cor 0"Branca" 1"Amarela" 2"Parda" 3"Negra" 4"Ignorado"

. 
end of do-file

. label val cor cor

. bysort ano: sum iniciovs,d

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> ano = 2019

                      Idade inicio VSA
-------------------------------------------------------------
      Percentiles      Smallest
 1%           12             12
 5%           14             12
10%           14             13       Obs                 197
25%           15             13       Sum of Wgt.         197

50%           18                      Mean           19.26904
                        Largest       Std. Dev.      6.298988
75%           21             42
90%           24             42       Variance       39.67725
95%           40             44       Skewness       2.200288
99%           44             44       Kurtosis       8.216014

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> ano = 2020

                      Idade inicio VSA
-------------------------------------------------------------
      Percentiles      Smallest
 1%           12             12
 5%           14             13
10%           14             14       Obs                  96
25%           16             14       Sum of Wgt.          96

50%           18                      Mean            18.6875
                        Largest       Std. Dev.      5.258202
75%           20             30
90%           23             40       Variance       27.64868
95%           25             40       Skewness       2.906675
99%           46             46       Kurtosis        13.9685

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> ano = 2021

                      Idade inicio VSA
-------------------------------------------------------------
      Percentiles      Smallest
 1%           14             14
 5%           14             14
10%           17             17       Obs                  21
25%           17             17       Sum of Wgt.          21

50%           18                      Mean           19.52381
                        Largest       Std. Dev.      3.310877
75%           22             23
90%           23             23       Variance        10.9619
95%           23             23       Skewness       .2872268
99%           27             27       Kurtosis       2.553357


. bysort ano: tab cid 4

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> ano = 2019
cid ambiguous abbreviation
r(111);

. bysort ano: tab cid4

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> ano = 2019

      CID 4 |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        263       93.59       93.59
          2 |         16        5.69       99.29
          3 |          2        0.71      100.00
------------+-----------------------------------
      Total |        281      100.00

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> ano = 2020

      CID 4 |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        144       91.14       91.14
          2 |         12        7.59       98.73
          3 |          2        1.27      100.00
------------+-----------------------------------
      Total |        158      100.00

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> ano = 2021

      CID 4 |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |         41       97.62       97.62
          2 |          1        2.38      100.00
------------+-----------------------------------
      Total |         42      100.00


. tab cid4 prepos, row cow chi exact
option cow not allowed
r(198);

. tab cid4 prepos, row col chi exact

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

Enumerating sample-space combinations:
stage 3:  enumerations = 1
stage 2:  enumerations = 2
stage 1:  enumerations = 0

           |  Pré - Pós COVID-19
     CID 4 | Pré COVID  Pós COVID |     Total
-----------+----------------------+----------
         1 |       303        145 |       448 
           |     67.63      32.37 |    100.00 
           |     93.81      91.77 |     93.14 
-----------+----------------------+----------
         2 |        18         11 |        29 
           |     62.07      37.93 |    100.00 
           |      5.57       6.96 |      6.03 
-----------+----------------------+----------
         3 |         2          2 |         4 
           |     50.00      50.00 |    100.00 
           |      0.62       1.27 |      0.83 
-----------+----------------------+----------
     Total |       323        158 |       481 
           |     67.15      32.85 |    100.00 
           |    100.00     100.00 |    100.00 

          Pearson chi2(2) =   0.9203   Pr = 0.631
           Fisher's exact =                 0.593

. tab prepos gesta , row col chi exact

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

Enumerating sample-space combinations:
stage 7:  enumerations = 1
stage 6:  enumerations = 3
stage 5:  enumerations = 12
stage 4:  enumerations = 94
stage 3:  enumerations = 679
stage 2:  enumerations = 4877
stage 1:  enumerations = 0

    Pré - Pós |                             Número de gestações
     COVID-19 |         1          2          3          4          5          6         10 |     Total
--------------+-----------------------------------------------------------------------------+----------
 Pré COVID-19 |        35         54         41         20         20          2          2 |       174 
              |     20.11      31.03      23.56      11.49      11.49       1.15       1.15 |    100.00 
              |     72.92      63.53      64.06      52.63      64.52      40.00     100.00 |     63.74 
--------------+-----------------------------------------------------------------------------+----------
 Pós COVID-19 |        13         31         23         18         11          3          0 |        99 
              |     13.13      31.31      23.23      18.18      11.11       3.03       0.00 |    100.00 
              |     27.08      36.47      35.94      47.37      35.48      60.00       0.00 |     36.26 
--------------+-----------------------------------------------------------------------------+----------
        Total |        48         85         64         38         31          5          2 |       273 
              |     17.58      31.14      23.44      13.92      11.36       1.83       0.73 |    100.00 
              |    100.00     100.00     100.00     100.00     100.00     100.00     100.00 |    100.00 

          Pearson chi2(6) =   6.1471   Pr = 0.407
           Fisher's exact =                 0.438

. tab prepos partovag , row col chi exact

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

Enumerating sample-space combinations:
stage 6:  enumerations = 1
stage 5:  enumerations = 3
stage 4:  enumerations = 14
stage 3:  enumerations = 65
stage 2:  enumerations = 292
stage 1:  enumerations = 0

    Pré - Pós |                     Número de partos vaginais
     COVID-19 |         1          2          3          4          5          6 |     Total
--------------+------------------------------------------------------------------+----------
 Pré COVID-19 |        20         18         17         13          9          0 |        77 
              |     25.97      23.38      22.08      16.88      11.69       0.00 |    100.00 
              |     68.97      66.67      58.62      68.42      64.29       0.00 |     64.17 
--------------+------------------------------------------------------------------+----------
 Pós COVID-19 |         9          9         12          6          5          2 |        43 
              |     20.93      20.93      27.91      13.95      11.63       4.65 |    100.00 
              |     31.03      33.33      41.38      31.58      35.71     100.00 |     35.83 
--------------+------------------------------------------------------------------+----------
        Total |        29         27         29         19         14          2 |       120 
              |     24.17      22.50      24.17      15.83      11.67       1.67 |    100.00 
              |    100.00     100.00     100.00     100.00     100.00     100.00 |    100.00 

          Pearson chi2(5) =   4.4828   Pr = 0.482
           Fisher's exact =                 0.565

. tab prepos partocesar , row col chi exact

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

Enumerating sample-space combinations:
stage 5:  enumerations = 1
stage 4:  enumerations = 2
stage 3:  enumerations = 6
stage 2:  enumerations = 47
stage 1:  enumerations = 0

    Pré - Pós |                Número de partos cesárea
     COVID-19 |         1          2          3          4          5 |     Total
--------------+-------------------------------------------------------+----------
 Pré COVID-19 |        58         33          5          0          2 |        98 
              |     59.18      33.67       5.10       0.00       2.04 |    100.00 
              |     72.50      55.00      41.67       0.00     100.00 |     63.23 
--------------+-------------------------------------------------------+----------
 Pós COVID-19 |        22         27          7          1          0 |        57 
              |     38.60      47.37      12.28       1.75       0.00 |    100.00 
              |     27.50      45.00      58.33     100.00       0.00 |     36.77 
--------------+-------------------------------------------------------+----------
        Total |        80         60         12          1          2 |       155 
              |     51.61      38.71       7.74       0.65       1.29 |    100.00 
              |    100.00     100.00     100.00     100.00     100.00 |    100.00 

          Pearson chi2(4) =   9.9869   Pr = 0.041
           Fisher's exact =                 0.025

. bysort prepos: sum gesta ,d

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pré COVID-19

                   Número de gestações
-------------------------------------------------------------
      Percentiles      Smallest
 1%            1              1
 5%            1              1
10%            1              1       Obs                 174
25%            2              1       Sum of Wgt.         174

50%            2                      Mean           2.747126
                        Largest       Std. Dev.      1.518429
75%            4              6
90%            5              6       Variance       2.305628
95%            5             10       Skewness       1.476039
99%           10             10       Kurtosis       7.290175

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pós COVID-19

                   Número de gestações
-------------------------------------------------------------
      Percentiles      Smallest
 1%            1              1
 5%            1              1
10%            1              1       Obs                  99
25%            2              1       Sum of Wgt.          99

50%            3                      Mean           2.919192
                        Largest       Std. Dev.      1.322311
75%            4              5
90%            5              6       Variance       1.748505
95%            5              6       Skewness       .4151633
99%            6              6       Kurtosis       2.358621


. bysort prepos: sum partovag ,d

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pré COVID-19

                 Número de partos vaginais
-------------------------------------------------------------
      Percentiles      Smallest
 1%            1              1
 5%            1              1
10%            1              1       Obs                  77
25%            1              1       Sum of Wgt.          77

50%            3                      Mean           2.649351
                        Largest       Std. Dev.      1.345228
75%            4              5
90%            5              5       Variance       1.809638
95%            5              5       Skewness       .2992092
99%            5              5       Kurtosis       1.911294

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pós COVID-19

                 Número de partos vaginais
-------------------------------------------------------------
      Percentiles      Smallest
 1%            1              1
 5%            1              1
10%            1              1       Obs                  43
25%            2              1       Sum of Wgt.          43

50%            3                      Mean           2.883721
                        Largest       Std. Dev.      1.450932
75%            4              5
90%            5              5       Variance       2.105205
95%            5              6       Skewness       .3936567
99%            6              6       Kurtosis       2.293783


. ttest partovag, by(prepos)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
Pré COVI |      77    2.649351    .1533029    1.345228    2.344021     2.95468
Pós COVI |      43    2.883721    .2212651    1.450932     2.43719    3.330252
---------+--------------------------------------------------------------------
combined |     120    2.733333    .1262103    1.382564    2.483424    2.983242
---------+--------------------------------------------------------------------
    diff |           -.2343703    .2634373               -.7560478    .2873073
------------------------------------------------------------------------------
    diff = mean(Pré COVI) - mean(Pós COVI)                        t =  -0.8897
Ho: diff = 0                                     degrees of freedom =      118

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.1877         Pr(|T| > |t|) = 0.3755          Pr(T > t) = 0.8123

. ttest gesta , by(prepos)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
Pré COVI |     174    2.747126    .1151118    1.518429    2.519922    2.974331
Pós COVI |      99    2.919192    .1328972    1.322311    2.655462    3.182922
---------+--------------------------------------------------------------------
combined |     273    2.809524    .0877745    1.450273     2.63672    2.982328
---------+--------------------------------------------------------------------
    diff |           -.1720655    .1826116               -.5315832    .1874523
------------------------------------------------------------------------------
    diff = mean(Pré COVI) - mean(Pós COVI)                        t =  -0.9422
Ho: diff = 0                                     degrees of freedom =      271

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.1735         Pr(|T| > |t|) = 0.3469          Pr(T > t) = 0.8265

. ttest partocesar , by(prepos)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
Pré COVI |      98    1.520408    .0784201    .7763195    1.364766     1.67605
Pós COVI |      57     1.77193    .0970025     .732353     1.57761    1.966249
---------+--------------------------------------------------------------------
combined |     155    1.612903    .0616673    .7677518     1.49108    1.734726
---------+--------------------------------------------------------------------
    diff |           -.2515217    .1266856               -.5018005   -.0012428
------------------------------------------------------------------------------
    diff = mean(Pré COVI) - mean(Pós COVI)                        t =  -1.9854
Ho: diff = 0                                     degrees of freedom =      153

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0244         Pr(|T| > |t|) = 0.0489          Pr(T > t) = 0.9756

. ttest forceps , by(prepos)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
Pré COVI |      23    1.478261    .2424314     1.16266    .9754889    1.981033
Pós COVI |      11    1.181818    .1219673    .4045199     .910058    1.453578
---------+--------------------------------------------------------------------
combined |      34    1.382353    .1689577    .9851844    1.038606      1.7261
---------+--------------------------------------------------------------------
    diff |            .2964427     .362994                -.442952    1.035837
------------------------------------------------------------------------------
    diff = mean(Pré COVI) - mean(Pós COVI)                        t =   0.8167
Ho: diff = 0                                     degrees of freedom =       32

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.7899         Pr(|T| > |t|) = 0.4202          Pr(T > t) = 0.2101

. ttest aborto , by(prepos)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
Pré COVI |      16        1.75    .2813657    1.125463    1.150283    2.349717
Pós COVI |      12    1.166667    .1666667    .5773503    .7998358    1.533498
---------+--------------------------------------------------------------------
combined |      28         1.5    .1818482    .9622504    1.126878    1.873122
---------+--------------------------------------------------------------------
    diff |            .5833333    .3565624               -.1495912    1.316258
------------------------------------------------------------------------------
    diff = mean(Pré COVI) - mean(Pós COVI)                        t =   1.6360
Ho: diff = 0                                     degrees of freedom =       26

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.9431         Pr(|T| > |t|) = 0.1139          Pr(T > t) = 0.0569

. ttest idade , by(prepos)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
Pré COVI |     399    54.85213    .5638548    11.26299    53.74362    55.96064
Pós COVI |     189    52.84656    .8171607     11.2341    51.23458    54.45854
---------+--------------------------------------------------------------------
combined |     588    54.20748    .4653093    11.28315    53.29361    55.12136
---------+--------------------------------------------------------------------
    diff |            2.005569    .9937287                .0538659    3.957273
------------------------------------------------------------------------------
    diff = mean(Pré COVI) - mean(Pós COVI)                        t =   2.0182
Ho: diff = 0                                     degrees of freedom =      586

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.9780         Pr(|T| > |t|) = 0.0440          Pr(T > t) = 0.0220

. ttest iniciovs , by(prepos)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
Pré COVI |     218    19.41284    .4332316    6.396588    18.55896    20.26672
Pós COVI |      96    18.41667    .4321678    4.234362    17.55871    19.27463
---------+--------------------------------------------------------------------
combined |     314    19.10828    .3291573    5.832683    18.46064    19.75592
---------+--------------------------------------------------------------------
    diff |            .9961774     .713364               -.4074351     2.39979
------------------------------------------------------------------------------
    diff = mean(Pré COVI) - mean(Pós COVI)                        t =   1.3965
Ho: diff = 0                                     degrees of freedom =      312

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.9182         Pr(|T| > |t|) = 0.1636          Pr(T > t) = 0.0818

. ttest coitarca , by(prepos)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
Pré COVI |       4       16.25    .4787136    .9574271    14.72652    17.77348
Pós COVI |      13    16.46154    .5014771    1.808101    15.36891    17.55416
---------+--------------------------------------------------------------------
combined |      17    16.41176    .3935026    1.622453    15.57758    17.24595
---------+--------------------------------------------------------------------
    diff |           -.2115385    .9565383               -2.250352    1.827275
------------------------------------------------------------------------------
    diff = mean(Pré COVI) - mean(Pós COVI)                        t =  -0.2212
Ho: diff = 0                                     degrees of freedom =       15

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.4140         Pr(|T| > |t|) = 0.8280          Pr(T > t) = 0.5860

. ttest idademenopausa , by(prepos)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
Pré COVI |     176    47.35227    .4578379    6.073906    46.44868    48.25587
Pós COVI |      87    47.09195    .6406927    5.975983     45.8183    48.36561
---------+--------------------------------------------------------------------
combined |     263    47.26616    .3719196    6.031523    46.53383    47.99849
---------+--------------------------------------------------------------------
    diff |            .2603187    .7918253                -1.29886    1.819498
------------------------------------------------------------------------------
    diff = mean(Pré COVI) - mean(Pós COVI)                        t =   0.3288
Ho: diff = 0                                     degrees of freedom =      261

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.6287         Pr(|T| > |t|) = 0.7426          Pr(T > t) = 0.3713

. ttest peso , by(prepos)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
Pré COVI |      75    75.91133    2.101826    18.20235    71.72335    80.09931
Pós COVI |      37    72.45784    2.076766    12.63247    68.24596    76.66971
---------+--------------------------------------------------------------------
combined |     112    74.77045    1.567816     16.5922    71.66371    77.87718
---------+--------------------------------------------------------------------
    diff |            3.453495    3.332243               -3.150229    10.05722
------------------------------------------------------------------------------
    diff = mean(Pré COVI) - mean(Pós COVI)                        t =   1.0364
Ho: diff = 0                                     degrees of freedom =      110

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.8489         Pr(|T| > |t|) = 0.3023          Pr(T > t) = 0.1511

. ttest pas , by(prepos)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
Pré COVI |      67    123.6119    2.493951    20.41387    118.6326    128.5913
Pós COVI |      27    125.1852    3.835669    19.93072    117.3009    133.0695
---------+--------------------------------------------------------------------
combined |      94    124.0638    2.081601    20.18187    119.9302    128.1975
---------+--------------------------------------------------------------------
    diff |           -1.573245    4.622536                 -10.754     7.60751
------------------------------------------------------------------------------
    diff = mean(Pré COVI) - mean(Pós COVI)                        t =  -0.3403
Ho: diff = 0                                     degrees of freedom =       92

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.3672         Pr(|T| > |t|) = 0.7344          Pr(T > t) = 0.6328

. ttest pad , by(prepos)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
Pré COVI |      58    73.72414    1.611324    12.27148    70.49752    76.95076
Pós COVI |      27    78.07407    2.187068    11.36434    73.57849    82.56966
---------+--------------------------------------------------------------------
combined |      85    75.10588    1.311981    12.09587    72.49686     77.7149
---------+--------------------------------------------------------------------
    diff |           -4.349936    2.794492               -9.908068    1.208196
------------------------------------------------------------------------------
    diff = mean(Pré COVI) - mean(Pós COVI)                        t =  -1.5566
Ho: diff = 0                                     degrees of freedom =       83

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0617         Pr(|T| > |t|) = 0.1234          Pr(T > t) = 0.9383

. ttest cid1 , by(prepos)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
Pré COVI |      25        1.04         .04          .2    .9574441    1.122556
Pós COVI |      13           1           0           0           1           1
---------+--------------------------------------------------------------------
combined |      38    1.026316    .0263158    .1622214    .9729949    1.079637
---------+--------------------------------------------------------------------
    diff |                 .04    .0558386               -.0732459    .1532459
------------------------------------------------------------------------------
    diff = mean(Pré COVI) - mean(Pós COVI)                        t =   0.7164
Ho: diff = 0                                     degrees of freedom =       36

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.7608         Pr(|T| > |t|) = 0.4784          Pr(T > t) = 0.2392

. histogram cid1, by(mes)

. histogram cid1 mes
too many variables specified
r(103);

. histogram cid1 over (mes)
variable over not found
r(111);

. histogram cid1, over (mes)
(bin=6, start=1, width=.16666667)
option over() not allowed
r(198);

. bysort mes: histogram cid1
histogram may not be combined with by
r(190);

. graph bar cid1 , over( prepos ) bargap(10) blabel(total) ytitle(mMol) title(Frequência de excitação-inicial) subtitle(Placebo (inici
parentheses do not balance
r(198);

. 
. > al e final) versus Tratado (inicial e final)) legend(off)
> is not a valid command name
r(199);

. graph bar cid1 , over( prepos ) bargap(10) blabel(total) ytitle(mMol) title(Frequência de excitação-inicial) subtitle(Placebo (inicial e final) versus Tratado
>  (inicial e final)) legend(off)

. histogram cid1, frequency by(prepos)

. histogram cid1, frequency by(prepos, total)

. histogram cid1, frequency
(bin=6, start=1, width=.16666667)

. histogram cid1, frequency by(prepos)

. graph bar (count), over(cid4) by(prepos)

. graph bar (count) cid1, by(prepos) bargap(10) blabel(total) ytitle(Escore) title(Grau de desconforto na penetração profunda) subtitle(Placebo (inicial e final
> ) versus Tratado (inicial e final)) legend(off)

. graph bar (count) cid1 (count) cid2 (count) , over(prepos) bargap(10) blabel(total) ytitle(Escore) title(Número de atendimentos Pré - Pós COVID-19) subtitle(A
> tendimentos Ambulatoriais (inicial e final) versus Tratado (inicial e final)) legend(off)
varlist required
r(100);

. graph bar (count) cid1 cid2 , over(prepos) bargap(10) blabel(total) ytitle(Escore) title(Número de atendimentos Pré - Pós COVID-19) subtitle(Atendimentos Ambu
> latoriais (inicial e final) versus Tratado (inicial e final)) legend(off)

. graph bar (count) cid1 cid2 , over(prepos) bargap(10) blabel(total) ytitle(Escore) title(Número de atendimentos Pré - Pós COVID-19) subtitle(Atendimentos Ambu
> latoriais (inicial e final) versus Tratado (inicial e final))

. graph bar (count) cid1 cid2 cid3 cid4, over(prepos) bargap(10) blabel(total) ytitle(Escore) title(Número de atendimentos Pré - Pós COVID-19) subtitle(Atendime
> ntos Ambulatoriais (inicial e final) versus Tratado (inicial e final))

. graph bar (count) cid1 cid2 cid3 cid4, over(prepos) bargap(10) blabel(total) ytitle(Número de atendimentos) title(Número de atendimentos Pré - Pós COVID-19) s
> ubtitle(Atendimentos Ambulatoriais)

. graph bar (count) cid1 cid2 cid3 cid4, over(ano) bargap(10) blabel(total) ytitle(Número de atendimentos) title(Número de atendimentos Pré - Pós COVID-19) subt
> itle(Atendimentos Ambulatoriais)

. graph bar (count) cid1 , over(mes) bargap(10) blabel(total) ytitle(Número de atendimentos) title(Número de atendimentos Pré - Pós COVID-19) subtitle(Atendimen
> tos Ambulatoriais)

. graph bar (count) cid1 , over(mes) bargap(10) blabel(total) ytitle(Número de atendimentos) title(Número de atendimentos Pré - Pós COVID-19) subtitle(CID-1)

. graph bar (count) cid1 , over(mes) bargap(10) blabel(total) ytitle(Número de atendimentos) title(Número de atendimentos Pré - Pós COVID-19) subtitle(CID-1, po
> r mês de atendimento)
option por not allowed
r(198);

. graph bar (count) cid1 , over(mes) bargap(10) blabel(total) ytitle(Número de atendimentos) title(Número de atendimentos Pré - Pós COVID-19) subtitle(CID-1 por
>  mês de atendimento)

. graph bar (count) cid2 , over(mes) bargap(10) blabel(total) ytitle(Número de atendimentos) title(Número de atendimentos Pré - Pós COVID-19) subtitle(CID-2 por
>  mês de atendimento)

. graph bar (count) cid3 , over(mes) bargap(10) blabel(total) ytitle(Número de atendimentos) title(Número de atendimentos Pré - Pós COVID-19) subtitle(CID-3 por
>  mês de atendimento)

. graph bar (count) cid4, over(mes) bargap(10) blabel(total) ytitle(Número de atendimentos) title(Número de atendimentos Pré - Pós COVID-19) subtitle(CID-4 por 
> mês de atendimento)

. graph bar (count) cid5, over(mes) bargap(10) blabel(total) ytitle(Número de atendimentos) title(Número de atendimentos Pré - Pós COVID-19) subtitle(CID-5 por 
> mês de atendimento)

. graph bar (count) cid1 cid2 cid3 cid4 cid5, over(prepos) bargap(10) blabel(total) ytitle(Escore) title(Número de atendimentos Pré - Pós COVID-19) subtitle(Ate
> ndimentos Ambulatoriais (inicial e final) versus Tratado (inicial e final))

. graph bar (count) cid1 cid2 cid3 cid4 cid5, over(prepos) bargap(10) blabel(total) ytitle(Número de atendimentos) title(Número de atendimentos Pré - Pós COVID-
> 19) subtitle(Atendimentos Ambulatoriais)

. graph bar (count) cid1 cid2 cid3 cid4 cid5, over(mes) bargap(10) blabel(total) ytitle(Número de atendimentos) title(Número de atendimentos Pré - Pós COVID-19)
>  subtitle(Atendimentos Ambulatoriais)

. graph bar (count) cid1 cid2 cid3 cid4 cid5, over(mes) bargap(10) ytitle(Número de atendimentos) title(Número de atendimentos Pré - Pós COVID-19) subtitle(Aten
> dimentos Ambulatoriais)

. graph bar (count) cid1 cid2 cid3 cid4 cid5, over(mes) ytitle(Número de atendimentos) title(Número de atendimentos Pré - Pós COVID-19) subtitle(Atendimentos Am
> bulatoriais)

. twoway cid1 mes
cid1 is not a twoway plot type
r(198);

. tab cor

        cor |      Freq.     Percent        Cum.
------------+-----------------------------------
     Branca |        441       75.00       75.00
    Amarela |         10        1.70       76.70
      Parda |         41        6.97       83.67
      Negra |         51        8.67       92.35
   Ignorado |         45        7.65      100.00
------------+-----------------------------------
      Total |        588      100.00

. bysort prepos: menop, d
command menop is unrecognized
r(199);

. bysort prepos: menop , d
command menop is unrecognized
r(199);

. bysort prepos: tab menop

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pré COVID-19

  Menopausa |      Freq.     Percent        Cum.
------------+-----------------------------------
        Não |        164       41.10       41.10
        Sim |        235       58.90      100.00
------------+-----------------------------------
      Total |        399      100.00

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pós COVID-19

  Menopausa |      Freq.     Percent        Cum.
------------+-----------------------------------
        Não |         84       44.44       44.44
        Sim |        105       55.56      100.00
------------+-----------------------------------
      Total |        189      100.00


. bysort prepos: idademenopausa , d
command idademenopausa is unrecognized
r(199);

. bysort prepos: has menop
command has is unrecognized
r(199);

. tab has

 Hipertensã |
 o arterial |      Freq.     Percent        Cum.
------------+-----------------------------------
        Não |        416       70.75       70.75
        Sim |        172       29.25      100.00
------------+-----------------------------------
      Total |        588      100.00

. ttest menop, by(prepos)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
Pré COVI |     399    .5889724    .0246627     .492638    .5404869     .637458
Pós COVI |     189    .5555556    .0362404    .4982238    .4840654    .6270457
---------+--------------------------------------------------------------------
combined |     588    .5782313     .020383    .4942624    .5381987    .6182639
---------+--------------------------------------------------------------------
    diff |            .0334169    .0436598               -.0523319    .1191657
------------------------------------------------------------------------------
    diff = mean(Pré COVI) - mean(Pós COVI)                        t =   0.7654
Ho: diff = 0                                     degrees of freedom =      586

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.7778         Pr(|T| > |t|) = 0.4443          Pr(T > t) = 0.2222

. bysort prepos: tab menopausa

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pré COVID-19
variable menopausa not found
r(111);

. bysort prepos: tab menop

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pré COVID-19

  Menopausa |      Freq.     Percent        Cum.
------------+-----------------------------------
        Não |        164       41.10       41.10
        Sim |        235       58.90      100.00
------------+-----------------------------------
      Total |        399      100.00

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pós COVID-19

  Menopausa |      Freq.     Percent        Cum.
------------+-----------------------------------
        Não |         84       44.44       44.44
        Sim |        105       55.56      100.00
------------+-----------------------------------
      Total |        189      100.00


. gen idadecat = idade

. recode idade cat min/50=0 50.1/max=1
unknown el cat in rule
r(198);

. recode idadecat min/50=0 50.1/max=1
(idadecat: 588 changes made)

. tab idadecat

   idadecat |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        181       30.78       30.78
          1 |        407       69.22      100.00
------------+-----------------------------------
      Total |        588      100.00

. gen idadecat2 = idade

. recode idadecat2 min/35=0 35.1/44=1 44.1/54=2 54.1/64=3 64.1/max=4
(idadecat2: 588 changes made)

. tab idadecat2

  idadecat2 |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |         39        6.63        6.63
          1 |         47        7.99       14.63
          2 |        192       32.65       47.28
          3 |        225       38.27       85.54
          4 |         85       14.46      100.00
------------+-----------------------------------
      Total |        588      100.00

. tab etnia
variable etnia not found
r(111);

. tab cor

        cor |      Freq.     Percent        Cum.
------------+-----------------------------------
     Branca |        441       75.00       75.00
    Amarela |         10        1.70       76.70
      Parda |         41        6.97       83.67
      Negra |         51        8.67       92.35
   Ignorado |         45        7.65      100.00
------------+-----------------------------------
      Total |        588      100.00

. gen etnia = cor

. recode etnia 0=0 1/max=1
(etnia: 137 changes made)

. tab etnia

      etnia |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        441       75.00       75.00
          1 |        147       25.00      100.00
------------+-----------------------------------
      Total |        588      100.00

. label define etnia 0"Branca" 1"Não branca"

. label val etnia etnia

. tab etnia

      etnia |      Freq.     Percent        Cum.
------------+-----------------------------------
     Branca |        441       75.00       75.00
 Não branca |        147       25.00      100.00
------------+-----------------------------------
      Total |        588      100.00

. sum idade, d

                            idade
-------------------------------------------------------------
      Percentiles      Smallest
 1%           22             14
 5%           32             17
10%           39             19       Obs                 588
25%           49             21       Sum of Wgt.         588

50%           55                      Mean           54.20748
                        Largest       Std. Dev.      11.28315
75%           61             81
90%           67             82       Variance       127.3095
95%           73             82       Skewness      -.4533984
99%           80             92       Kurtosis       4.007542

. tab idadecat

   idadecat |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        181       30.78       30.78
          1 |        407       69.22      100.00
------------+-----------------------------------
      Total |        588      100.00

. tab idadecat2

  idadecat2 |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |         39        6.63        6.63
          1 |         47        7.99       14.63
          2 |        192       32.65       47.28
          3 |        225       38.27       85.54
          4 |         85       14.46      100.00
------------+-----------------------------------
      Total |        588      100.00

. label define idadecat2 0"Até 35 anos" 1"35 a 44 anos" 2"45 a 54 anos" 3"55 a 64 anos" 4"Acima de 65 anos"

. label val idadecat2 idadecat2

. tab idadecat2

       idadecat2 |      Freq.     Percent        Cum.
-----------------+-----------------------------------
     Até 35 anos |         39        6.63        6.63
    35 a 44 anos |         47        7.99       14.63
    45 a 54 anos |        192       32.65       47.28
    55 a 64 anos |        225       38.27       85.54
Acima de 65 anos |         85       14.46      100.00
-----------------+-----------------------------------
           Total |        588      100.00

. tab vidasexual

Vida sexual |
    ativa / |
    inativa |      Freq.     Percent        Cum.
------------+-----------------------------------
    Inativa |        117       38.61       38.61
      Ativa |        186       61.39      100.00
------------+-----------------------------------
      Total |        303      100.00

. sort seq

. *(1 variable, 588 observations pasted into data editor)

. destring multimorb, replace
multimorb already numeric; no replace

. gen multimorbcat = multimorb

. recode multimorbcat min/1=0 1.1/max=1
(multimorbcat: 405 changes made)

. tab multimorbcat

multimorbca |
          t |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        341       57.99       57.99
          1 |        247       42.01      100.00
------------+-----------------------------------
      Total |        588      100.00

. bysort prepos: tab multimorbcat

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pré COVID-19

multimorbca |
          t |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        227       56.89       56.89
          1 |        172       43.11      100.00
------------+-----------------------------------
      Total |        399      100.00

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pós COVID-19

multimorbca |
          t |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        114       60.32       60.32
          1 |         75       39.68      100.00
------------+-----------------------------------
      Total |        189      100.00


. bysort prepos: sum idade, d

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pré COVID-19

                            idade
-------------------------------------------------------------
      Percentiles      Smallest
 1%           21             14
 5%           31             17
10%           41             19       Obs                 399
25%           50             21       Sum of Wgt.         399

50%           55                      Mean           54.85213
                        Largest       Std. Dev.      11.26299
75%           62             80
90%           68             81       Variance        126.855
95%           74             82       Skewness      -.6341466
99%           80             82       Kurtosis       4.258792

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pós COVID-19

                            idade
-------------------------------------------------------------
      Percentiles      Smallest
 1%           22             21
 5%           33             22
10%           37             24       Obs                 189
25%           48             29       Sum of Wgt.         189

50%           53                      Mean           52.84656
                        Largest       Std. Dev.       11.2341
75%           59             76
90%           66             79       Variance       126.2051
95%           72             81       Skewness      -.0836087
99%           81             92       Kurtosis       3.784427


. swtest idade
command swtest is unrecognized
r(199);

. sktest idade

                    Skewness/Kurtosis tests for Normality
                                                          ------ joint ------
    Variable |        Obs  Pr(Skewness)  Pr(Kurtosis) adj chi2(2)   Prob>chi2
-------------+---------------------------------------------------------------
       idade |        588     0.0000        0.0003       26.68         0.0000

. help shapiro

. swilk idade iniciovs gesta partovag partocesar forceps aborto menarca coitarca idademenopausa peso altura imc pas pad

                   Shapiro-Wilk W test for normal data

    Variable |        Obs       W           V         z       Prob>z
-------------+------------------------------------------------------
       idade |        588    0.97276     10.605     5.717    0.00000
    iniciovs |        314    0.76420     52.311     9.309    0.00000
       gesta |        273    0.93561     12.621     5.924    0.00000
    partovag |        120    0.96088      3.764     2.970    0.00149
  partocesar |        155    0.90254     11.663     5.578    0.00000
     forceps |         34    0.63643     12.695     5.295    0.00000
      aborto |         28    0.69342      9.258     4.582    0.00000
     menarca |        223    0.85726     23.417     7.295    0.00000
    coitarca |         17    0.93550      1.363     0.617    0.26867
idademenop~a |        263    0.91029     17.013     6.610    0.00000
        peso |        112    0.95183      4.374     3.294    0.00049
      altura |        105    0.98809      1.024     0.053    0.47874
         imc |        105    0.94931      4.359     3.275    0.00053
         pas |         94    0.92788      5.655     3.831    0.00006
         pad |         85    0.97828      1.567     0.988    0.16156

. ranksum idade,by(prepos)

Two-sample Wilcoxon rank-sum (Mann-Whitney) test

      prepos |      obs    rank sum    expected
-------------+---------------------------------
 Pré COVID-1 |      399      122494    117505.5
 Pós COVID-1 |      189       50672     55660.5
-------------+---------------------------------
    combined |      588      173166      173166

unadjusted variance  3701423.25
adjustment for ties    -5669.33
                     ----------
adjusted variance    3695753.92

Ho: idade(prepos==Pré COVID-19) = idade(prepos==Pós COVID-19)
             z =   2.595
    Prob > |z| =   0.0095

. bysort prepos: sum idademenopausa , d

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pré COVID-19

                     Idade da menopausa
-------------------------------------------------------------
      Percentiles      Smallest
 1%           23             23
 5%           35             23
10%           40             27       Obs                 176
25%           45             31       Sum of Wgt.         176

50%           48                      Mean           47.35227
                        Largest       Std. Dev.      6.073906
75%         51.5             56
90%           54             56       Variance       36.89234
95%           55             56       Skewness      -1.409849
99%           56             57       Kurtosis       5.805821

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pós COVID-19

                     Idade da menopausa
-------------------------------------------------------------
      Percentiles      Smallest
 1%           28             28
 5%           33             32
10%           39             32       Obs                  87
25%           44             32       Sum of Wgt.          87

50%           49                      Mean           47.09195
                        Largest       Std. Dev.      5.975983
75%           51             55
90%           54             55       Variance       35.71238
95%           55             56       Skewness      -1.037334
99%           56             56       Kurtosis       3.912314


. ranksum idademenopausa ,by(prepos)

Two-sample Wilcoxon rank-sum (Mann-Whitney) test

      prepos |      obs    rank sum    expected
-------------+---------------------------------
 Pré COVID-1 |      176       23412       23232
 Pós COVID-1 |       87       11304       11484
-------------+---------------------------------
    combined |      263       34716       34716

unadjusted variance   336864.00
adjustment for ties    -1827.84
                     ----------
adjusted variance     335036.16

Ho: idadem~a(prepos==Pré COVID-19) = idadem~a(prepos==Pós COVID-19)
             z =   0.311
    Prob > |z| =   0.7558

. bysort prepos: sum peso , d

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pré COVID-19

                            Peso
-------------------------------------------------------------
      Percentiles      Smallest
 1%         46.5           46.5
 5%         49.5             48
10%         54.5          48.55       Obs                  75
25%           61           49.5       Sum of Wgt.          75

50%           76                      Mean           75.91133
                        Largest       Std. Dev.      18.20235
75%         85.3            107
90%        100.8         107.85       Variance       331.3254
95%          107          128.4       Skewness       .7256471
99%          132            132       Kurtosis       3.523864

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pós COVID-19

                            Peso
-------------------------------------------------------------
      Percentiles      Smallest
 1%           53             53
 5%           55             55
10%        58.15             58       Obs                  37
25%           64          58.15       Sum of Wgt.          37

50%         68.3                      Mean           72.45784
                        Largest       Std. Dev.      12.63247
75%        81.65             87
90%           87           89.9       Variance       159.5794
95%          102            102       Skewness       .7457562
99%          106            106       Kurtosis       3.063758


. ranksum peso ,by(prepos)

Two-sample Wilcoxon rank-sum (Mann-Whitney) test

      prepos |      obs    rank sum    expected
-------------+---------------------------------
 Pré COVID-1 |       75      4356.5      4237.5
 Pós COVID-1 |       37      1971.5      2090.5
-------------+---------------------------------
    combined |      112        6328        6328

unadjusted variance    26131.25
adjustment for ties       -8.59
                     ----------
adjusted variance      26122.66

Ho: peso(prepos==Pré COVID-19) = peso(prepos==Pós COVID-19)
             z =   0.736
    Prob > |z| =   0.4616

. save "C:\Users\LIM58\Dropbox\Ginecologia e Obstetrícia\Isabel - Atenção primária a saúde\atendimentos.dta", replace
file C:\Users\LIM58\Dropbox\Ginecologia e Obstetrícia\Isabel - Atenção primária a saúde\atendimentos.dta saved

. exit, clear
----------------------------------------------------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  C:\Users\LIM58\Dropbox\Ginecologia e Obstetrícia\Isabel - Atenção primária a saúde\atend.log
  log type:  text
 opened on:  15 Jun 2021, 12:31:45

.  bysort prepos: sum altura , d

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pré COVID-19

                           Altura
-------------------------------------------------------------
      Percentiles      Smallest
 1%         1.35           1.35
 5%         1.45          1.355
10%         1.47           1.43       Obs                  70
25%         1.52           1.45       Sum of Wgt.          70

50%         1.57                      Mean             1.5645
                        Largest       Std. Dev.      .0790347
75%          1.6           1.68
90%         1.66            1.7       Variance       .0062465
95%         1.68           1.75       Skewness       .0280271
99%          1.8            1.8       Kurtosis       4.030536

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pós COVID-19

                           Altura
-------------------------------------------------------------
      Percentiles      Smallest
 1%         1.45           1.45
 5%         1.46           1.46
10%         1.52           1.51       Obs                  35
25%         1.53           1.52       Sum of Wgt.          35

50%         1.58                      Mean           1.584143
                        Largest       Std. Dev.      .0633905
75%         1.64           1.67
90%         1.67           1.67       Variance       .0040184
95%          1.7            1.7       Skewness        .057766
99%         1.71           1.71       Kurtosis       2.487811


. ranksum altura ,by(prepos)

Two-sample Wilcoxon rank-sum (Mann-Whitney) test

      prepos |      obs    rank sum    expected
-------------+---------------------------------
 Pré COVID-1 |       70      3514.5        3710
 Pós COVID-1 |       35      2050.5        1855
-------------+---------------------------------
    combined |      105        5565        5565

unadjusted variance    21641.67
adjustment for ties      -84.92
                     ----------
adjusted variance      21556.75

Ho: altura(prepos==Pré COVID-19) = altura(prepos==Pós COVID-19)
             z =  -1.332
    Prob > |z| =   0.1830

.  bysort prepos: sum imc , d

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pré COVID-19

                             IMC
-------------------------------------------------------------
      Percentiles      Smallest
 1%        20.78          20.78
 5%        22.12          21.42
10%       22.755          22.11       Obs                  70
25%        26.04          22.12       Sum of Wgt.          70

50%       30.855                      Mean             30.812
                        Largest       Std. Dev.      6.254463
75%        34.08          43.11
90%       40.505          44.73       Variance       39.11831
95%        43.11          46.68       Skewness        .701282
99%        47.56          47.56       Kurtosis       3.177013

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pós COVID-19

                             IMC
-------------------------------------------------------------
      Percentiles      Smallest
 1%        21.89          21.89
 5%        22.31          22.31
10%        22.66          22.57       Obs                  35
25%         24.9          22.66       Sum of Wgt.          35

50%        28.54                      Mean             29.286
                        Largest       Std. Dev.      5.338134
75%        32.88          36.57
90%        36.57          36.94       Variance       28.49567
95%        37.91          37.91       Skewness       .6785642
99%        44.12          44.12       Kurtosis       2.938907


. ranksum imc ,by(prepos)

Two-sample Wilcoxon rank-sum (Mann-Whitney) test

      prepos |      obs    rank sum    expected
-------------+---------------------------------
 Pré COVID-1 |       70      3864.5        3710
 Pós COVID-1 |       35      1700.5        1855
-------------+---------------------------------
    combined |      105        5565        5565

unadjusted variance    21641.67
adjustment for ties       -0.79
                     ----------
adjusted variance      21640.88

Ho: imc(prepos==Pré COVID-19) = imc(prepos==Pós COVID-19)
             z =   1.050
    Prob > |z| =   0.2936

.  bysort prepos: sum pas , d

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pré COVID-19

                             PAS
-------------------------------------------------------------
      Percentiles      Smallest
 1%           83             83
 5%          100             98
10%          101             99       Obs                  67
25%          109            100       Sum of Wgt.          67

50%          120                      Mean           123.6119
                        Largest       Std. Dev.      20.41387
75%          132            160
90%          150            179       Variance       416.7259
95%          160            180       Skewness       1.218485
99%          196            196       Kurtosis       5.129985

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pós COVID-19

                             PAS
-------------------------------------------------------------
      Percentiles      Smallest
 1%           95             95
 5%           98             98
10%          100            100       Obs                  27
25%          113            100       Sum of Wgt.          27

50%          121                      Mean           125.1852
                        Largest       Std. Dev.      19.93072
75%          134            150
90%          155            155       Variance       397.2336
95%          162            162       Skewness       .8799443
99%          180            180       Kurtosis       3.677021


. ranksum pas ,by(prepos)

Two-sample Wilcoxon rank-sum (Mann-Whitney) test

      prepos |      obs    rank sum    expected
-------------+---------------------------------
 Pré COVID-1 |       67        3146      3182.5
 Pós COVID-1 |       27        1319      1282.5
-------------+---------------------------------
    combined |       94        4465        4465

unadjusted variance    14321.25
adjustment for ties      -32.80
                     ----------
adjusted variance      14288.45

Ho: pas(prepos==Pré COVID-19) = pas(prepos==Pós COVID-19)
             z =  -0.305
    Prob > |z| =   0.7601

.  bysort prepos: sum pad , d

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pré COVID-19

                             PAD
-------------------------------------------------------------
      Percentiles      Smallest
 1%           48             48
 5%           53             53
10%           58             53       Obs                  58
25%           67             57       Sum of Wgt.          58

50%           72                      Mean           73.72414
                        Largest       Std. Dev.      12.27148
75%           80            100
90%           92            100       Variance       150.5892
95%          100            100       Skewness       .5726692
99%          106            106       Kurtosis       3.409331

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pós COVID-19

                             PAD
-------------------------------------------------------------
      Percentiles      Smallest
 1%           50             50
 5%           60             60
10%           62             62       Obs                  27
25%           71             66       Sum of Wgt.          27

50%           80                      Mean           78.07407
                        Largest       Std. Dev.      11.36434
75%           83             90
90%           92             92       Variance       129.1481
95%          100            100       Skewness      -.2812071
99%          100            100       Kurtosis       3.279194


. ranksum pad ,by(prepos)

Two-sample Wilcoxon rank-sum (Mann-Whitney) test

      prepos |      obs    rank sum    expected
-------------+---------------------------------
 Pré COVID-1 |       58      2269.5        2494
 Pós COVID-1 |       27      1385.5        1161
-------------+---------------------------------
    combined |       85        3655        3655

unadjusted variance    11223.00
adjustment for ties      -46.61
                     ----------
adjusted variance      11176.39

Ho: pad(prepos==Pré COVID-19) = pad(prepos==Pós COVID-19)
             z =  -2.124
    Prob > |z| =   0.0337

.  bysort prepos: sum iniciovs , d

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pré COVID-19

                      Idade inicio VSA
-------------------------------------------------------------
      Percentiles      Smallest
 1%           13             12
 5%           14             12
10%           14             13       Obs                 218
25%           16             13       Sum of Wgt.         218

50%           18                      Mean           19.41284
                        Largest       Std. Dev.      6.396588
75%           21             42
90%           25             42       Variance       40.91633
95%           40             44       Skewness       2.162187
99%           42             44       Kurtosis       7.843618

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pós COVID-19

                      Idade inicio VSA
-------------------------------------------------------------
      Percentiles      Smallest
 1%           12             12
 5%           14             14
10%           14             14       Obs                  96
25%           16             14       Sum of Wgt.          96

50%           18                      Mean           18.41667
                        Largest       Std. Dev.      4.234362
75%           20             24
90%           23             25       Variance       17.92982
95%           23             27       Skewness       2.965893
99%           46             46       Kurtosis       19.91151


. sort seq

. *(1 variable, 588 observations pasted into data editor)

.  bysort prepos: sum paridade , d

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pré COVID-19

                          paridade
-------------------------------------------------------------
      Percentiles      Smallest
 1%            1              1
 5%            1              1
10%            1              1       Obs                 152
25%            1              1       Sum of Wgt.         152

50%            2                      Mean           2.546053
                        Largest       Std. Dev.      1.513029
75%            3              6
90%            5              6       Variance       2.289256
95%            6              6       Skewness       .9015536
99%            6              7       Kurtosis       2.921274

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pós COVID-19

                          paridade
-------------------------------------------------------------
      Percentiles      Smallest
 1%            1              1
 5%            1              1
10%            1              1       Obs                  89
25%            2              1       Sum of Wgt.          89

50%            2                      Mean           2.674157
                        Largest       Std. Dev.      1.329519
75%            3              6
90%            5              6       Variance        1.76762
95%            6              6       Skewness       .9634891
99%            6              6       Kurtosis       3.307329


. label variable paridade "Número de partos"

.  bysort prepos: sum paridade , d

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pré COVID-19

                      Número de partos
-------------------------------------------------------------
      Percentiles      Smallest
 1%            1              1
 5%            1              1
10%            1              1       Obs                 152
25%            1              1       Sum of Wgt.         152

50%            2                      Mean           2.546053
                        Largest       Std. Dev.      1.513029
75%            3              6
90%            5              6       Variance       2.289256
95%            6              6       Skewness       .9015536
99%            6              7       Kurtosis       2.921274

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pós COVID-19

                      Número de partos
-------------------------------------------------------------
      Percentiles      Smallest
 1%            1              1
 5%            1              1
10%            1              1       Obs                  89
25%            2              1       Sum of Wgt.          89

50%            2                      Mean           2.674157
                        Largest       Std. Dev.      1.329519
75%            3              6
90%            5              6       Variance        1.76762
95%            6              6       Skewness       .9634891
99%            6              6       Kurtosis       3.307329


. ranksum paridade ,by(prepos)

Two-sample Wilcoxon rank-sum (Mann-Whitney) test

      prepos |      obs    rank sum    expected
-------------+---------------------------------
 Pré COVID-1 |      152     17761.5       18392
 Pós COVID-1 |       89     11399.5       10769
-------------+---------------------------------
    combined |      241       29161       29161

unadjusted variance   272814.67
adjustment for ties   -17343.04
                     ----------
adjusted variance     255471.63

Ho: paridade(prepos==Pré COVID-19) = paridade(prepos==Pós COVID-19)
             z =  -1.247
    Prob > |z| =   0.2122

.  bysort prepos: sum menarca , d

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pré COVID-19

                        Idade menarca
-------------------------------------------------------------
      Percentiles      Smallest
 1%            9              8
 5%           10              9
10%           11              9       Obs                 145
25%           12              9       Sum of Wgt.         145

50%           13                      Mean           13.17241
                        Largest       Std. Dev.      2.590969
75%           14             17
90%           15             21       Variance       6.713123
95%           17             28       Skewness       2.693996
99%           28             28       Kurtosis       16.25153

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pós COVID-19

                        Idade menarca
-------------------------------------------------------------
      Percentiles      Smallest
 1%            8              8
 5%           10              8
10%           10              9       Obs                  78
25%           12             10       Sum of Wgt.          78

50%           13                      Mean            12.9359
                        Largest       Std. Dev.      2.008681
75%           14             16
90%           16             17       Variance       4.034799
95%           16             17       Skewness      -.1148847
99%           17             17       Kurtosis        2.74197


. ranksum menarca ,by(prepos)

Two-sample Wilcoxon rank-sum (Mann-Whitney) test

      prepos |      obs    rank sum    expected
-------------+---------------------------------
 Pré COVID-1 |      145     16264.5       16240
 Pós COVID-1 |       78      8711.5        8736
-------------+---------------------------------
    combined |      223       24976       24976

unadjusted variance   211120.00
adjustment for ties    -5772.43
                     ----------
adjusted variance     205347.57

Ho: menarca(prepos==Pré COVID-19) = menarca(prepos==Pós COVID-19)
             z =   0.054
    Prob > |z| =   0.9569

.  bysort prepos: sum coitarca , d

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pré COVID-19

                       Idade coitarca
-------------------------------------------------------------
      Percentiles      Smallest
 1%           15             15
 5%           15             16
10%           15             17       Obs                   4
25%         15.5             17       Sum of Wgt.           4

50%         16.5                      Mean              16.25
                        Largest       Std. Dev.      .9574271
75%           17             15
90%           17             16       Variance       .9166667
95%           17             17       Skewness      -.4933822
99%           17             17       Kurtosis       1.628099

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pós COVID-19

                       Idade coitarca
-------------------------------------------------------------
      Percentiles      Smallest
 1%           12             12
 5%           12             15
10%           15             15       Obs                  13
25%           15             15       Sum of Wgt.          13

50%           17                      Mean           16.46154
                        Largest       Std. Dev.      1.808101
75%           17             17
90%           18             18       Variance       3.269231
95%           19             18       Skewness      -1.079929
99%           19             19       Kurtosis       3.912042


. ranksum coitarca ,by(prepos)

Two-sample Wilcoxon rank-sum (Mann-Whitney) test

      prepos |      obs    rank sum    expected
-------------+---------------------------------
 Pré COVID-1 |        4        30.5          36
 Pós COVID-1 |       13       122.5         117
-------------+---------------------------------
    combined |       17         153         153

unadjusted variance       78.00
adjustment for ties       -9.08
                     ----------
adjusted variance         68.92

Ho: coitarca(prepos==Pré COVID-19) = coitarca(prepos==Pós COVID-19)
             z =  -0.663
    Prob > |z| =   0.5076

. tab vidasexual prepos, row col chi exact

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

      Vida |
    sexual |
   ativa / |  Pré - Pós COVID-19
   inativa | Pré COVID  Pós COVID |     Total
-----------+----------------------+----------
   Inativa |        84         33 |       117 
           |     71.79      28.21 |    100.00 
           |     40.00      35.48 |     38.61 
-----------+----------------------+----------
     Ativa |       126         60 |       186 
           |     67.74      32.26 |    100.00 
           |     60.00      64.52 |     61.39 
-----------+----------------------+----------
     Total |       210         93 |       303 
           |     69.31      30.69 |    100.00 
           |    100.00     100.00 |    100.00 

          Pearson chi2(1) =   0.5546   Pr = 0.456
           Fisher's exact =                 0.523
   1-sided Fisher's exact =                 0.270

.  bysort prepos: tab vidasexual , d

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pré COVID-19
option d not allowed
r(198);

.  bysort prepos: tab vidasexual

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pré COVID-19

Vida sexual |
    ativa / |
    inativa |      Freq.     Percent        Cum.
------------+-----------------------------------
    Inativa |         84       40.00       40.00
      Ativa |        126       60.00      100.00
------------+-----------------------------------
      Total |        210      100.00

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pós COVID-19

Vida sexual |
    ativa / |
    inativa |      Freq.     Percent        Cum.
------------+-----------------------------------
    Inativa |         33       35.48       35.48
      Ativa |         60       64.52      100.00
------------+-----------------------------------
      Total |         93      100.00


.  bysort prepos: tab obesidade

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pré COVID-19

  Obesidade |      Freq.     Percent        Cum.
------------+-----------------------------------
        Não |        374       93.73       93.73
        Sim |         25        6.27      100.00
------------+-----------------------------------
      Total |        399      100.00

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pós COVID-19

  Obesidade |      Freq.     Percent        Cum.
------------+-----------------------------------
        Não |        173       91.53       91.53
        Sim |         16        8.47      100.00
------------+-----------------------------------
      Total |        189      100.00


. tab obesidade prepos, row col chi exact

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

           |  Pré - Pós COVID-19
 Obesidade | Pré COVID  Pós COVID |     Total
-----------+----------------------+----------
       Não |       374        173 |       547 
           |     68.37      31.63 |    100.00 
           |     93.73      91.53 |     93.03 
-----------+----------------------+----------
       Sim |        25         16 |        41 
           |     60.98      39.02 |    100.00 
           |      6.27       8.47 |      6.97 
-----------+----------------------+----------
     Total |       399        189 |       588 
           |     67.86      32.14 |    100.00 
           |    100.00     100.00 |    100.00 

          Pearson chi2(1) =   0.9569   Pr = 0.328
           Fisher's exact =                 0.386
   1-sided Fisher's exact =                 0.209

. tab fogacho prepos, row col chi exact

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

           |  Pré - Pós COVID-19
   Fogacho | Pré COVID  Pós COVID |     Total
-----------+----------------------+----------
       Não |       306        148 |       454 
           |     67.40      32.60 |    100.00 
           |     76.69      78.31 |     77.21 
-----------+----------------------+----------
       Sim |        93         41 |       134 
           |     69.40      30.60 |    100.00 
           |     23.31      21.69 |     22.79 
-----------+----------------------+----------
     Total |       399        189 |       588 
           |     67.86      32.14 |    100.00 
           |    100.00     100.00 |    100.00 

          Pearson chi2(1) =   0.1901   Pr = 0.663
           Fisher's exact =                 0.752
   1-sided Fisher's exact =                 0.373

. tab menop prepos, row col chi exact

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

           |  Pré - Pós COVID-19
 Menopausa | Pré COVID  Pós COVID |     Total
-----------+----------------------+----------
       Não |       164         84 |       248 
           |     66.13      33.87 |    100.00 
           |     41.10      44.44 |     42.18 
-----------+----------------------+----------
       Sim |       235        105 |       340 
           |     69.12      30.88 |    100.00 
           |     58.90      55.56 |     57.82 
-----------+----------------------+----------
     Total |       399        189 |       588 
           |     67.86      32.14 |    100.00 
           |    100.00     100.00 |    100.00 

          Pearson chi2(1) =   0.5872   Pr = 0.443
           Fisher's exact =                 0.475
   1-sided Fisher's exact =                 0.249

. sort seq

. *(1 variable, 588 observations pasted into data editor)

. tab totalcid prepos, row col chi exact

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

Enumerating sample-space combinations:
stage 4:  enumerations = 1
stage 3:  enumerations = 3
stage 2:  enumerations = 19
stage 1:  enumerations = 0

           |  Pré - Pós COVID-19
  totalcid | Pré COVID  Pós COVID |     Total
-----------+----------------------+----------
         1 |       306        125 |       431 
           |     71.00      29.00 |    100.00 
           |     81.60      73.10 |     78.94 
-----------+----------------------+----------
         2 |        60         42 |       102 
           |     58.82      41.18 |    100.00 
           |     16.00      24.56 |     18.68 
-----------+----------------------+----------
         3 |         8          3 |        11 
           |     72.73      27.27 |    100.00 
           |      2.13       1.75 |      2.01 
-----------+----------------------+----------
         4 |         1          1 |         2 
           |     50.00      50.00 |    100.00 
           |      0.27       0.58 |      0.37 
-----------+----------------------+----------
     Total |       375        171 |       546 
           |     68.68      31.32 |    100.00 
           |    100.00     100.00 |    100.00 

          Pearson chi2(3) =   6.0914   Pr = 0.107
           Fisher's exact =                 0.078

. tab totalcid, by(prepos)
option by() not allowed
r(198);

. tab prepos

   Pré - Pós |
    COVID-19 |      Freq.     Percent        Cum.
-------------+-----------------------------------
Pré COVID-19 |        399       67.86       67.86
Pós COVID-19 |        189       32.14      100.00
-------------+-----------------------------------
       Total |        588      100.00

. ttest totalcid by(prepos)
factor variables and time-series operators not allowed
r(101);

. tab prepos totalcid, row col chi exact

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

Enumerating sample-space combinations:
stage 4:  enumerations = 1
stage 3:  enumerations = 3
stage 2:  enumerations = 19
stage 1:  enumerations = 0

    Pré - Pós |                  totalcid
     COVID-19 |         1          2          3          4 |     Total
--------------+--------------------------------------------+----------
 Pré COVID-19 |       306         60          8          1 |       375 
              |     81.60      16.00       2.13       0.27 |    100.00 
              |     71.00      58.82      72.73      50.00 |     68.68 
--------------+--------------------------------------------+----------
 Pós COVID-19 |       125         42          3          1 |       171 
              |     73.10      24.56       1.75       0.58 |    100.00 
              |     29.00      41.18      27.27      50.00 |     31.32 
--------------+--------------------------------------------+----------
        Total |       431        102         11          2 |       546 
              |     78.94      18.68       2.01       0.37 |    100.00 
              |    100.00     100.00     100.00     100.00 |    100.00 

          Pearson chi2(3) =   6.0914   Pr = 0.107
           Fisher's exact =                 0.078

. ranksum totalcid ,by(prepos)

Two-sample Wilcoxon rank-sum (Mann-Whitney) test

      prepos |      obs    rank sum    expected
-------------+---------------------------------
 Pré COVID-1 |      375     99903.5    102562.5
 Pós COVID-1 |      171     49427.5     46768.5
-------------+---------------------------------
    combined |      546      149331      149331

unadjusted variance  2923031.25
adjustment for ties   -1.46e+06
                     ----------
adjusted variance    1466191.93

Ho: totalcid(prepos==Pré COVID-19) = totalcid(prepos==Pós COVID-19)
             z =  -2.196
    Prob > |z| =   0.0281

. ranksum cid1 ,by(prepos)

Two-sample Wilcoxon rank-sum (Mann-Whitney) test

      prepos |      obs    rank sum    expected
-------------+---------------------------------
 Pré COVID-1 |       25         494       487.5
 Pós COVID-1 |       13         247       253.5
-------------+---------------------------------
    combined |       38         741         741

unadjusted variance     1056.25
adjustment for ties     -975.00
                     ----------
adjusted variance         81.25

Ho: cid1(prepos==Pré COVID-19) = cid1(prepos==Pós COVID-19)
             z =   0.721
    Prob > |z| =   0.4708

. tab prepos cid1, row col chi exact

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

    Pré - Pós |         CID 1
     COVID-19 |         1          2 |     Total
--------------+----------------------+----------
 Pré COVID-19 |        24          1 |        25 
              |     96.00       4.00 |    100.00 
              |     64.86     100.00 |     65.79 
--------------+----------------------+----------
 Pós COVID-19 |        13          0 |        13 
              |    100.00       0.00 |    100.00 
              |     35.14       0.00 |     34.21 
--------------+----------------------+----------
        Total |        37          1 |        38 
              |     97.37       2.63 |    100.00 
              |    100.00     100.00 |    100.00 

          Pearson chi2(1) =   0.5341   Pr = 0.465
           Fisher's exact =                 1.000
   1-sided Fisher's exact =                 0.658

. tab cid1 prepos, row col chi exact

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

           |  Pré - Pós COVID-19
     CID 1 | Pré COVID  Pós COVID |     Total
-----------+----------------------+----------
         1 |        24         13 |        37 
           |     64.86      35.14 |    100.00 
           |     96.00     100.00 |     97.37 
-----------+----------------------+----------
         2 |         1          0 |         1 
           |    100.00       0.00 |    100.00 
           |      4.00       0.00 |      2.63 
-----------+----------------------+----------
     Total |        25         13 |        38 
           |     65.79      34.21 |    100.00 
           |    100.00     100.00 |    100.00 

          Pearson chi2(1) =   0.5341   Pr = 0.465
           Fisher's exact =                 1.000
   1-sided Fisher's exact =                 0.658

. tab cid2 prepos, row col chi exact

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

           |  Pré - Pós COVID-19
     CID 2 | Pré COVID  Pós COVID |     Total
-----------+----------------------+----------
         1 |        10          7 |        17 
           |     58.82      41.18 |    100.00 
           |    100.00     100.00 |    100.00 
-----------+----------------------+----------
     Total |        10          7 |        17 
           |     58.82      41.18 |    100.00 
           |    100.00     100.00 |    100.00 


. ranksum cid2 ,by(prepos)

Two-sample Wilcoxon rank-sum (Mann-Whitney) test

      prepos |      obs    rank sum    expected
-------------+---------------------------------
 Pré COVID-1 |       10          90          90
 Pós COVID-1 |        7          63          63
-------------+---------------------------------
    combined |       17         153         153

unadjusted variance      105.00
adjustment for ties     -105.00
                     ----------
adjusted variance          0.00

Ho: cid2(prepos==Pré COVID-19) = cid2(prepos==Pós COVID-19)
             z =       .
    Prob > |z| =        .

. tab cid2 prepos, row col chi exact

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

           |  Pré - Pós COVID-19
     CID 2 | Pré COVID  Pós COVID |     Total
-----------+----------------------+----------
         1 |        10          7 |        17 
           |     58.82      41.18 |    100.00 
           |    100.00     100.00 |    100.00 
-----------+----------------------+----------
     Total |        10          7 |        17 
           |     58.82      41.18 |    100.00 
           |    100.00     100.00 |    100.00 


. ranksum cid2 ,by(prepos)

Two-sample Wilcoxon rank-sum (Mann-Whitney) test

      prepos |      obs    rank sum    expected
-------------+---------------------------------
 Pré COVID-1 |       10          90          90
 Pós COVID-1 |        7          63          63
-------------+---------------------------------
    combined |       17         153         153

unadjusted variance      105.00
adjustment for ties     -105.00
                     ----------
adjusted variance          0.00

Ho: cid2(prepos==Pré COVID-19) = cid2(prepos==Pós COVID-19)
             z =       .
    Prob > |z| =        .

. ttest totalcid, by(prepos)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
Pré COVI |     375    1.210667    .0245251    .4749256    1.162442    1.258891
Pós COVI |     171    1.298246    .0405456    .5302024    1.218208    1.378283
---------+--------------------------------------------------------------------
combined |     546    1.238095    .0211451    .4940891    1.196559    1.279631
---------+--------------------------------------------------------------------
    diff |           -.0875789    .0454791                -.176915    .0017571
------------------------------------------------------------------------------
    diff = mean(Pré COVI) - mean(Pós COVI)                        t =  -1.9257
Ho: diff = 0                                     degrees of freedom =      544

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0273         Pr(|T| > |t|) = 0.0547          Pr(T > t) = 0.9727

. tab prepos totalcid, row col chi exact

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

Enumerating sample-space combinations:
stage 4:  enumerations = 1
stage 3:  enumerations = 3
stage 2:  enumerations = 19
stage 1:  enumerations = 0

    Pré - Pós |                  totalcid
     COVID-19 |         1          2          3          4 |     Total
--------------+--------------------------------------------+----------
 Pré COVID-19 |       306         60          8          1 |       375 
              |     81.60      16.00       2.13       0.27 |    100.00 
              |     71.00      58.82      72.73      50.00 |     68.68 
--------------+--------------------------------------------+----------
 Pós COVID-19 |       125         42          3          1 |       171 
              |     73.10      24.56       1.75       0.58 |    100.00 
              |     29.00      41.18      27.27      50.00 |     31.32 
--------------+--------------------------------------------+----------
        Total |       431        102         11          2 |       546 
              |     78.94      18.68       2.01       0.37 |    100.00 
              |    100.00     100.00     100.00     100.00 |    100.00 

          Pearson chi2(3) =   6.0914   Pr = 0.107
           Fisher's exact =                 0.078

. ranksum totalcid ,by(prepos)

Two-sample Wilcoxon rank-sum (Mann-Whitney) test

      prepos |      obs    rank sum    expected
-------------+---------------------------------
 Pré COVID-1 |      375     99903.5    102562.5
 Pós COVID-1 |      171     49427.5     46768.5
-------------+---------------------------------
    combined |      546      149331      149331

unadjusted variance  2923031.25
adjustment for ties   -1.46e+06
                     ----------
adjusted variance    1466191.93

Ho: totalcid(prepos==Pré COVID-19) = totalcid(prepos==Pós COVID-19)
             z =  -2.196
    Prob > |z| =   0.0281

. ranksum cid2 ,by(prepos)

Two-sample Wilcoxon rank-sum (Mann-Whitney) test

      prepos |      obs    rank sum    expected
-------------+---------------------------------
 Pré COVID-1 |       10          90          90
 Pós COVID-1 |        7          63          63
-------------+---------------------------------
    combined |       17         153         153

unadjusted variance      105.00
adjustment for ties     -105.00
                     ----------
adjusted variance          0.00

Ho: cid2(prepos==Pré COVID-19) = cid2(prepos==Pós COVID-19)
             z =       .
    Prob > |z| =        .

. ttest cid2, by(prepos)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
Pré COVI |      10           1           0           0           1           1
Pós COVI |       7           1           0           0           1           1
---------+--------------------------------------------------------------------
combined |      17           1           0           0           1           1
---------+--------------------------------------------------------------------
    diff |                   0           0                       0           0
------------------------------------------------------------------------------
    diff = mean(Pré COVI) - mean(Pós COVI)                        t =        .
Ho: diff = 0                                     degrees of freedom =       15

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) =      .         Pr(|T| > |t|) =      .          Pr(T > t) =      .

. sort seq

. *(5 variables, 588 observations pasted into data editor)

. label variable totalcid "CID-1"

. label variable totalcid "Número total de diagnósticos"

. label variable dcid1 "CID-1"

. label variable dcid2 "CID-2"

. label variable dcid3 "CID-3"

. label variable dcid4 "CID-4"

. label variable dcid5 "CID-5"

. label define dcid1 dcid2 dcid3 dcid5 dcid5simnao
invalid syntax
r(198);

. label define simnao 0"Não" 1"Sim"
label simnao already defined
r(110);

. label define dcid1 dcid2 dcid3 dcid5 dcid5 simnao
invalid syntax
r(198);

. label val dcid1 dcid2 dcid3 dcid5 dcid5 simnao

. tab dcid1

      CID-1 |      Freq.     Percent        Cum.
------------+-----------------------------------
        Não |        550       93.54       93.54
        Sim |         38        6.46      100.00
------------+-----------------------------------
      Total |        588      100.00

. ranksum cid1 ,by(prepos)

Two-sample Wilcoxon rank-sum (Mann-Whitney) test

      prepos |      obs    rank sum    expected
-------------+---------------------------------
 Pré COVID-1 |       25         494       487.5
 Pós COVID-1 |       13         247       253.5
-------------+---------------------------------
    combined |       38         741         741

unadjusted variance     1056.25
adjustment for ties     -975.00
                     ----------
adjusted variance         81.25

Ho: cid1(prepos==Pré COVID-19) = cid1(prepos==Pós COVID-19)
             z =   0.721
    Prob > |z| =   0.4708

. tab cid1 prepos, row col chi exact

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

           |  Pré - Pós COVID-19
     CID 1 | Pré COVID  Pós COVID |     Total
-----------+----------------------+----------
         1 |        24         13 |        37 
           |     64.86      35.14 |    100.00 
           |     96.00     100.00 |     97.37 
-----------+----------------------+----------
         2 |         1          0 |         1 
           |    100.00       0.00 |    100.00 
           |      4.00       0.00 |      2.63 
-----------+----------------------+----------
     Total |        25         13 |        38 
           |     65.79      34.21 |    100.00 
           |    100.00     100.00 |    100.00 

          Pearson chi2(1) =   0.5341   Pr = 0.465
           Fisher's exact =                 1.000
   1-sided Fisher's exact =                 0.658

. tab dcid1 prepos, row col chi exact

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

           |  Pré - Pós COVID-19
     CID-1 | Pré COVID  Pós COVID |     Total
-----------+----------------------+----------
       Não |       374        176 |       550 
           |     68.00      32.00 |    100.00 
           |     93.73      93.12 |     93.54 
-----------+----------------------+----------
       Sim |        25         13 |        38 
           |     65.79      34.21 |    100.00 
           |      6.27       6.88 |      6.46 
-----------+----------------------+----------
     Total |       399        189 |       588 
           |     67.86      32.14 |    100.00 
           |    100.00     100.00 |    100.00 

          Pearson chi2(1) =   0.0796   Pr = 0.778
           Fisher's exact =                 0.858
   1-sided Fisher's exact =                 0.452

. tab dcid2 prepos, row col chi exact

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

           |  Pré - Pós COVID-19
     CID-2 | Pré COVID  Pós COVID |     Total
-----------+----------------------+----------
       Não |       389        182 |       571 
           |     68.13      31.87 |    100.00 
           |     97.49      96.30 |     97.11 
-----------+----------------------+----------
       Sim |        10          7 |        17 
           |     58.82      41.18 |    100.00 
           |      2.51       3.70 |      2.89 
-----------+----------------------+----------
     Total |       399        189 |       588 
           |     67.86      32.14 |    100.00 
           |    100.00     100.00 |    100.00 

          Pearson chi2(1) =   0.6550   Pr = 0.418
           Fisher's exact =                 0.436
   1-sided Fisher's exact =                 0.286

. tab dcid3 prepos, row col chi exact

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

           |  Pré - Pós COVID-19
     CID-3 | Pré COVID  Pós COVID |     Total
-----------+----------------------+----------
       Não |       393        182 |       575 
           |     68.35      31.65 |    100.00 
           |     98.50      96.30 |     97.79 
-----------+----------------------+----------
       Sim |         6          7 |        13 
           |     46.15      53.85 |    100.00 
           |      1.50       3.70 |      2.21 
-----------+----------------------+----------
     Total |       399        189 |       588 
           |     67.86      32.14 |    100.00 
           |    100.00     100.00 |    100.00 

          Pearson chi2(1) =   2.8709   Pr = 0.090
           Fisher's exact =                 0.129
   1-sided Fisher's exact =                 0.085

. tab dcid4 prepos, row col chi exact

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

           |  Pré - Pós COVID-19
     CID-4 | Pré COVID  Pós COVID |     Total
-----------+----------------------+----------
         0 |        76         30 |       106 
           |     71.70      28.30 |    100.00 
           |     19.05      15.87 |     18.03 
-----------+----------------------+----------
         1 |       323        159 |       482 
           |     67.01      32.99 |    100.00 
           |     80.95      84.13 |     81.97 
-----------+----------------------+----------
     Total |       399        189 |       588 
           |     67.86      32.14 |    100.00 
           |    100.00     100.00 |    100.00 

          Pearson chi2(1) =   0.8747   Pr = 0.350
           Fisher's exact =                 0.421
   1-sided Fisher's exact =                 0.207

. label define dcid4 simnao
invalid syntax
r(198);

. label val dcid4 simnao

. tab dcid4 prepos, row col chi exact

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

           |  Pré - Pós COVID-19
     CID-4 | Pré COVID  Pós COVID |     Total
-----------+----------------------+----------
       Não |        76         30 |       106 
           |     71.70      28.30 |    100.00 
           |     19.05      15.87 |     18.03 
-----------+----------------------+----------
       Sim |       323        159 |       482 
           |     67.01      32.99 |    100.00 
           |     80.95      84.13 |     81.97 
-----------+----------------------+----------
     Total |       399        189 |       588 
           |     67.86      32.14 |    100.00 
           |    100.00     100.00 |    100.00 

          Pearson chi2(1) =   0.8747   Pr = 0.350
           Fisher's exact =                 0.421
   1-sided Fisher's exact =                 0.207

. tab menop prepos, row col chi exact

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

           |  Pré - Pós COVID-19
 Menopausa | Pré COVID  Pós COVID |     Total
-----------+----------------------+----------
       Não |       164         84 |       248 
           |     66.13      33.87 |    100.00 
           |     41.10      44.44 |     42.18 
-----------+----------------------+----------
       Sim |       235        105 |       340 
           |     69.12      30.88 |    100.00 
           |     58.90      55.56 |     57.82 
-----------+----------------------+----------
     Total |       399        189 |       588 
           |     67.86      32.14 |    100.00 
           |    100.00     100.00 |    100.00 

          Pearson chi2(1) =   0.5872   Pr = 0.443
           Fisher's exact =                 0.475
   1-sided Fisher's exact =                 0.249

. tab dcid5 prepos, row col chi exact

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

           |  Pré - Pós COVID-19
     CID-5 | Pré COVID  Pós COVID |     Total
-----------+----------------------+----------
       Não |       332        168 |       500 
           |     66.40      33.60 |    100.00 
           |     83.21      88.89 |     85.03 
-----------+----------------------+----------
       Sim |        67         21 |        88 
           |     76.14      23.86 |    100.00 
           |     16.79      11.11 |     14.97 
-----------+----------------------+----------
     Total |       399        189 |       588 
           |     67.86      32.14 |    100.00 
           |    100.00     100.00 |    100.00 

          Pearson chi2(1) =   3.2523   Pr = 0.071
           Fisher's exact =                 0.083
   1-sided Fisher's exact =                 0.044

. tab dcid1 if menopausa==1 prepos
menopausa not found
r(111);

. tab dcid1 if menopa==1 prepos
menopa not found
r(111);

. tab dcid1 if menop==1 prepos
invalid 'prepos' 
r(198);

. bysort prepos: tab dcid1 if menop==1

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pré COVID-19

      CID-1 |      Freq.     Percent        Cum.
------------+-----------------------------------
        Não |        215       91.49       91.49
        Sim |         20        8.51      100.00
------------+-----------------------------------
      Total |        235      100.00

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pós COVID-19

      CID-1 |      Freq.     Percent        Cum.
------------+-----------------------------------
        Não |         96       91.43       91.43
        Sim |          9        8.57      100.00
------------+-----------------------------------
      Total |        105      100.00


. bysort prepos: tab dcid1 if menop

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pré COVID-19

      CID-1 |      Freq.     Percent        Cum.
------------+-----------------------------------
        Não |        215       91.49       91.49
        Sim |         20        8.51      100.00
------------+-----------------------------------
      Total |        235      100.00

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pós COVID-19

      CID-1 |      Freq.     Percent        Cum.
------------+-----------------------------------
        Não |         96       91.43       91.43
        Sim |          9        8.57      100.00
------------+-----------------------------------
      Total |        105      100.00


. bysort prepos: tab dcid1 if menop, row col chi exact

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pré COVID-19
option row not allowed
r(198);

. bysort prepos: tab dcid1 menop

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pré COVID-19

           |       Menopausa
     CID-1 |       Não        Sim |     Total
-----------+----------------------+----------
       Não |       159        215 |       374 
       Sim |         5         20 |        25 
-----------+----------------------+----------
     Total |       164        235 |       399 


----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pós COVID-19

           |       Menopausa
     CID-1 |       Não        Sim |     Total
-----------+----------------------+----------
       Não |        80         96 |       176 
       Sim |         4          9 |        13 
-----------+----------------------+----------
     Total |        84        105 |       189 



. bysort prepos: tab dcid1 if menop==1

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pré COVID-19

      CID-1 |      Freq.     Percent        Cum.
------------+-----------------------------------
        Não |        215       91.49       91.49
        Sim |         20        8.51      100.00
------------+-----------------------------------
      Total |        235      100.00

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pós COVID-19

      CID-1 |      Freq.     Percent        Cum.
------------+-----------------------------------
        Não |         96       91.43       91.43
        Sim |          9        8.57      100.00
------------+-----------------------------------
      Total |        105      100.00


. bysort prepos: tab dcid1 if menop==0

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pré COVID-19

      CID-1 |      Freq.     Percent        Cum.
------------+-----------------------------------
        Não |        159       96.95       96.95
        Sim |          5        3.05      100.00
------------+-----------------------------------
      Total |        164      100.00

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pós COVID-19

      CID-1 |      Freq.     Percent        Cum.
------------+-----------------------------------
        Não |         80       95.24       95.24
        Sim |          4        4.76      100.00
------------+-----------------------------------
      Total |         84      100.00


. bysort prepos: tab dcid1 menop

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pré COVID-19

           |       Menopausa
     CID-1 |       Não        Sim |     Total
-----------+----------------------+----------
       Não |       159        215 |       374 
       Sim |         5         20 |        25 
-----------+----------------------+----------
     Total |       164        235 |       399 


----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pós COVID-19

           |       Menopausa
     CID-1 |       Não        Sim |     Total
-----------+----------------------+----------
       Não |        80         96 |       176 
       Sim |         4          9 |        13 
-----------+----------------------+----------
     Total |        84        105 |       189 



. bysort prepos: tab menop

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pré COVID-19

  Menopausa |      Freq.     Percent        Cum.
------------+-----------------------------------
        Não |        164       41.10       41.10
        Sim |        235       58.90      100.00
------------+-----------------------------------
      Total |        399      100.00

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pós COVID-19

  Menopausa |      Freq.     Percent        Cum.
------------+-----------------------------------
        Não |         84       44.44       44.44
        Sim |        105       55.56      100.00
------------+-----------------------------------
      Total |        189      100.00


. bysort prepos: tab menop dcid1

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pré COVID-19

           |         CID-1
 Menopausa |       Não        Sim |     Total
-----------+----------------------+----------
       Não |       159          5 |       164 
       Sim |       215         20 |       235 
-----------+----------------------+----------
     Total |       374         25 |       399 


----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pós COVID-19

           |         CID-1
 Menopausa |       Não        Sim |     Total
-----------+----------------------+----------
       Não |        80          4 |        84 
       Sim |        96          9 |       105 
-----------+----------------------+----------
     Total |       176         13 |       189 



. bysort prepos: tab dcid1 menop

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pré COVID-19

           |       Menopausa
     CID-1 |       Não        Sim |     Total
-----------+----------------------+----------
       Não |       159        215 |       374 
       Sim |         5         20 |        25 
-----------+----------------------+----------
     Total |       164        235 |       399 


----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pós COVID-19

           |       Menopausa
     CID-1 |       Não        Sim |     Total
-----------+----------------------+----------
       Não |        80         96 |       176 
       Sim |         4          9 |        13 
-----------+----------------------+----------
     Total |        84        105 |       189 



. bysort prepos: tab dcid2 menop

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pré COVID-19

           |       Menopausa
     CID-2 |       Não        Sim |     Total
-----------+----------------------+----------
       Não |       161        228 |       389 
       Sim |         3          7 |        10 
-----------+----------------------+----------
     Total |       164        235 |       399 


----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pós COVID-19

           |       Menopausa
     CID-2 |       Não        Sim |     Total
-----------+----------------------+----------
       Não |        79        103 |       182 
       Sim |         5          2 |         7 
-----------+----------------------+----------
     Total |        84        105 |       189 



. bysort prepos: tab dcid3 menop

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pré COVID-19

           |       Menopausa
     CID-3 |       Não        Sim |     Total
-----------+----------------------+----------
       Não |       158        235 |       393 
       Sim |         6          0 |         6 
-----------+----------------------+----------
     Total |       164        235 |       399 


----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pós COVID-19

           |       Menopausa
     CID-3 |       Não        Sim |     Total
-----------+----------------------+----------
       Não |        81        101 |       182 
       Sim |         3          4 |         7 
-----------+----------------------+----------
     Total |        84        105 |       189 



. bysort prepos: tab dcid4 menop

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pré COVID-19

           |       Menopausa
     CID-4 |       Não        Sim |     Total
-----------+----------------------+----------
       Não |        40         36 |        76 
       Sim |       124        199 |       323 
-----------+----------------------+----------
     Total |       164        235 |       399 


----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pós COVID-19

           |       Menopausa
     CID-4 |       Não        Sim |     Total
-----------+----------------------+----------
       Não |        20         10 |        30 
       Sim |        64         95 |       159 
-----------+----------------------+----------
     Total |        84        105 |       189 



. bysort prepos: tab dcid5 menop

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pré COVID-19

           |       Menopausa
     CID-5 |       Não        Sim |     Total
-----------+----------------------+----------
       Não |       127        205 |       332 
       Sim |        37         30 |        67 
-----------+----------------------+----------
     Total |       164        235 |       399 


----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pós COVID-19

           |       Menopausa
     CID-5 |       Não        Sim |     Total
-----------+----------------------+----------
       Não |        72         96 |       168 
       Sim |        12          9 |        21 
-----------+----------------------+----------
     Total |        84        105 |       189 



. ranksum dcid1 ,by(prepos)

Two-sample Wilcoxon rank-sum (Mann-Whitney) test

      prepos |      obs    rank sum    expected
-------------+---------------------------------
 Pré COVID-1 |      399    117274.5    117505.5
 Pós COVID-1 |      189     55891.5     55660.5
-------------+---------------------------------
    combined |      588      173166      173166

unadjusted variance  3701423.25
adjustment for ties   -3.03e+06
                     ----------
adjusted variance     671247.83

Ho: dcid1(prepos==Pré COVID-19) = dcid1(prepos==Pós COVID-19)
             z =  -0.282
    Prob > |z| =   0.7780

. tab menopausa dcid1 if prepos==1
variable menopausa not found
r(111);

. tab menop dcid1 if prepos==1

           |         CID-1
 Menopausa |       Não        Sim |     Total
-----------+----------------------+----------
       Não |        80          4 |        84 
       Sim |        96          9 |       105 
-----------+----------------------+----------
     Total |       176         13 |       189 


. tab menop dcid1 if prepos==1, row col chi exact

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

           |         CID-1
 Menopausa |       Não        Sim |     Total
-----------+----------------------+----------
       Não |        80          4 |        84 
           |     95.24       4.76 |    100.00 
           |     45.45      30.77 |     44.44 
-----------+----------------------+----------
       Sim |        96          9 |       105 
           |     91.43       8.57 |    100.00 
           |     54.55      69.23 |     55.56 
-----------+----------------------+----------
     Total |       176         13 |       189 
           |     93.12       6.88 |    100.00 
           |    100.00     100.00 |    100.00 

          Pearson chi2(1) =   1.0573   Pr = 0.304
           Fisher's exact =                 0.392
   1-sided Fisher's exact =                 0.232

. tab prepos dcid1 if menop==1, row col chi exact

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

    Pré - Pós |         CID-1
     COVID-19 |       Não        Sim |     Total
--------------+----------------------+----------
 Pré COVID-19 |       215         20 |       235 
              |     91.49       8.51 |    100.00 
              |     69.13      68.97 |     69.12 
--------------+----------------------+----------
 Pós COVID-19 |        96          9 |       105 
              |     91.43       8.57 |    100.00 
              |     30.87      31.03 |     30.88 
--------------+----------------------+----------
        Total |       311         29 |       340 
              |     91.47       8.53 |    100.00 
              |    100.00     100.00 |    100.00 

          Pearson chi2(1) =   0.0003   Pr = 0.985
           Fisher's exact =                 1.000
   1-sided Fisher's exact =                 0.567

. tab prepos dcid2 if menop==1, row col chi exact

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

    Pré - Pós |         CID-2
     COVID-19 |       Não        Sim |     Total
--------------+----------------------+----------
 Pré COVID-19 |       228          7 |       235 
              |     97.02       2.98 |    100.00 
              |     68.88      77.78 |     69.12 
--------------+----------------------+----------
 Pós COVID-19 |       103          2 |       105 
              |     98.10       1.90 |    100.00 
              |     31.12      22.22 |     30.88 
--------------+----------------------+----------
        Total |       331          9 |       340 
              |     97.35       2.65 |    100.00 
              |    100.00     100.00 |    100.00 

          Pearson chi2(1) =   0.3248   Pr = 0.569
           Fisher's exact =                 0.727
   1-sided Fisher's exact =                 0.438

. tab prepos dcid3 if menop==1, row col chi exact

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

    Pré - Pós |         CID-3
     COVID-19 |       Não        Sim |     Total
--------------+----------------------+----------
 Pré COVID-19 |       235          0 |       235 
              |    100.00       0.00 |    100.00 
              |     69.94       0.00 |     69.12 
--------------+----------------------+----------
 Pós COVID-19 |       101          4 |       105 
              |     96.19       3.81 |    100.00 
              |     30.06     100.00 |     30.88 
--------------+----------------------+----------
        Total |       336          4 |       340 
              |     98.82       1.18 |    100.00 
              |    100.00     100.00 |    100.00 

          Pearson chi2(1) =   9.0590   Pr = 0.003
           Fisher's exact =                 0.009
   1-sided Fisher's exact =                 0.009

. bysort prepos: tab dcid4 menop

----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pré COVID-19

           |       Menopausa
     CID-4 |       Não        Sim |     Total
-----------+----------------------+----------
       Não |        40         36 |        76 
       Sim |       124        199 |       323 
-----------+----------------------+----------
     Total |       164        235 |       399 


----------------------------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pós COVID-19

           |       Menopausa
     CID-4 |       Não        Sim |     Total
-----------+----------------------+----------
       Não |        20         10 |        30 
       Sim |        64         95 |       159 
-----------+----------------------+----------
     Total |        84        105 |       189 



. tab prepos dcid4 if menop==1, row col chi exact

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

    Pré - Pós |         CID-4
     COVID-19 |       Não        Sim |     Total
--------------+----------------------+----------
 Pré COVID-19 |        36        199 |       235 
              |     15.32      84.68 |    100.00 
              |     78.26      67.69 |     69.12 
--------------+----------------------+----------
 Pós COVID-19 |        10         95 |       105 
              |      9.52      90.48 |    100.00 
              |     21.74      32.31 |     30.88 
--------------+----------------------+----------
        Total |        46        294 |       340 
              |     13.53      86.47 |    100.00 
              |    100.00     100.00 |    100.00 

          Pearson chi2(1) =   2.0835   Pr = 0.149
           Fisher's exact =                 0.172
   1-sided Fisher's exact =                 0.100

. tab prepos dcid4 if menop==0, row col chi exact

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

    Pré - Pós |         CID-4
     COVID-19 |       Não        Sim |     Total
--------------+----------------------+----------
 Pré COVID-19 |        40        124 |       164 
              |     24.39      75.61 |    100.00 
              |     66.67      65.96 |     66.13 
--------------+----------------------+----------
 Pós COVID-19 |        20         64 |        84 
              |     23.81      76.19 |    100.00 
              |     33.33      34.04 |     33.87 
--------------+----------------------+----------
        Total |        60        188 |       248 
              |     24.19      75.81 |    100.00 
              |    100.00     100.00 |    100.00 

          Pearson chi2(1) =   0.0102   Pr = 0.919
           Fisher's exact =                 1.000
   1-sided Fisher's exact =                 0.526

. tab prepos menop if dcid4==1, row col chi exact

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

    Pré - Pós |       Menopausa
     COVID-19 |       Não        Sim |     Total
--------------+----------------------+----------
 Pré COVID-19 |       124        199 |       323 
              |     38.39      61.61 |    100.00 
              |     65.96      67.69 |     67.01 
--------------+----------------------+----------
 Pós COVID-19 |        64         95 |       159 
              |     40.25      59.75 |    100.00 
              |     34.04      32.31 |     32.99 
--------------+----------------------+----------
        Total |       188        294 |       482 
              |     39.00      61.00 |    100.00 
              |    100.00     100.00 |    100.00 

          Pearson chi2(1) =   0.1552   Pr = 0.694
           Fisher's exact =                 0.693
   1-sided Fisher's exact =                 0.383

. tab prepos dcid4 if menop==1, row col chi exact

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

    Pré - Pós |         CID-4
     COVID-19 |       Não        Sim |     Total
--------------+----------------------+----------
 Pré COVID-19 |        36        199 |       235 
              |     15.32      84.68 |    100.00 
              |     78.26      67.69 |     69.12 
--------------+----------------------+----------
 Pós COVID-19 |        10         95 |       105 
              |      9.52      90.48 |    100.00 
              |     21.74      32.31 |     30.88 
--------------+----------------------+----------
        Total |        46        294 |       340 
              |     13.53      86.47 |    100.00 
              |    100.00     100.00 |    100.00 

          Pearson chi2(1) =   2.0835   Pr = 0.149
           Fisher's exact =                 0.172
   1-sided Fisher's exact =                 0.100

. tab prepos dcid5 if menop==1, row col chi exact

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

    Pré - Pós |         CID-5
     COVID-19 |       Não        Sim |     Total
--------------+----------------------+----------
 Pré COVID-19 |       205         30 |       235 
              |     87.23      12.77 |    100.00 
              |     68.11      76.92 |     69.12 
--------------+----------------------+----------
 Pós COVID-19 |        96          9 |       105 
              |     91.43       8.57 |    100.00 
              |     31.89      23.08 |     30.88 
--------------+----------------------+----------
        Total |       301         39 |       340 
              |     88.53      11.47 |    100.00 
              |    100.00     100.00 |    100.00 

          Pearson chi2(1) =   1.2574   Pr = 0.262
           Fisher's exact =                 0.357
   1-sided Fisher's exact =                 0.175

. logistic prepos dcid1

Logistic regression                             Number of obs     =        588
                                                LR chi2(1)        =       0.08
                                                Prob > chi2       =     0.7789
Log likelihood = -369.19024                     Pseudo R2         =     0.0001

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       dcid1 |      1.105   .3911119     0.28   0.778     .5521816    2.211276
       _cons |   .4705882    .043016    -8.25   0.000     .3933993    .5629225
------------------------------------------------------------------------------

. logistic prepos dcid4

Logistic regression                             Number of obs     =        588
                                                LR chi2(1)        =       0.89
                                                Prob > chi2       =     0.3454
Log likelihood = -368.78448                     Pseudo R2         =     0.0012

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       dcid4 |   1.247059   .2947824     0.93   0.350     .7846538    1.981964
       _cons |   .3947368   .0851125    -4.31   0.000     .2586861    .6023408
------------------------------------------------------------------------------

. logistic prepos obesidade

Logistic regression                             Number of obs     =        588
                                                LR chi2(1)        =       0.93
                                                Prob > chi2       =     0.3353
Log likelihood = -368.76552                     Pseudo R2         =     0.0013

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   obesidade |   1.383584   .4608687     0.97   0.330     .7202315    2.657901
       _cons |   .4625668   .0425314    -8.38   0.000     .3862865    .5539103
------------------------------------------------------------------------------

. logistic prepos fogacho

Logistic regression                             Number of obs     =        588
                                                LR chi2(1)        =       0.19
                                                Prob > chi2       =     0.6618
Log likelihood =   -369.134                     Pseudo R2         =     0.0003

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     fogacho |   .9115084     .19372    -0.44   0.663     .6009765    1.382496
       _cons |   .4836601   .0484258    -7.25   0.000     .3974797     .588526
------------------------------------------------------------------------------

. logistic prepos multimorbcat

Logistic regression                             Number of obs     =        588
                                                LR chi2(1)        =       0.62
                                                Prob > chi2       =     0.4312
Log likelihood = -368.91991                     Pseudo R2         =     0.0008

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
multimorbcat |   .8682681   .1561061    -0.79   0.432     .6104023     1.23507
       _cons |   .5022026   .0576489    -6.00   0.000     .4010216    .6289124
------------------------------------------------------------------------------

. logistic prepos menop

Logistic regression                             Number of obs     =        588
                                                LR chi2(1)        =       0.59
                                                Prob > chi2       =     0.4440
Log likelihood =  -368.9367                     Pseudo R2         =     0.0008

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       menop |   .8723404   .1555152    -0.77   0.444     .6150909    1.237179
       _cons |   .5121951   .0687226    -4.99   0.000     .3937562    .6662596
------------------------------------------------------------------------------

. logistic prepos menop regiao

Logistic regression                             Number of obs     =        517
                                                LR chi2(2)        =       1.95
                                                Prob > chi2       =     0.3777
Log likelihood = -319.68305                     Pseudo R2         =     0.0030

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       menop |   .8004986   .1562006    -1.14   0.254     .5460927    1.173424
      regiao |   1.218971   .2375823     1.02   0.310     .8319378     1.78606
       _cons |    .460486   .0750943    -4.76   0.000     .3345074    .6339093
------------------------------------------------------------------------------

. logistic prepos regiao

Logistic regression                             Number of obs     =        517
                                                LR chi2(1)        =       0.65
                                                Prob > chi2       =     0.4210
Log likelihood =   -320.333                     Pseudo R2         =     0.0010

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      regiao |   1.165519   .2220655     0.80   0.421     .8023068    1.693161
       _cons |   .4171428   .0581206    -6.28   0.000     .3174583    .5481292
------------------------------------------------------------------------------

. logistic prepos cid1
note: cid1 != 1 predicts failure perfectly
      cid1 dropped and 1 obs not used


Logistic regression                             Number of obs     =         37
                                                LR chi2(0)        =      -0.00
                                                Prob > chi2       =          .
Log likelihood = -23.986329                     Pseudo R2         =    -0.0000

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        cid1 |          1  (omitted)
       _cons |   .5416667    .186533    -1.78   0.075     .2758068    1.063798
------------------------------------------------------------------------------

. logistic prepos vidasexual

Logistic regression                             Number of obs     =        303
                                                LR chi2(1)        =       0.56
                                                Prob > chi2       =     0.4550
Log likelihood = -186.55768                     Pseudo R2         =     0.0015

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  vidasexual |   1.212121   .3133069     0.74   0.457     .7303466    2.011699
       _cons |   .3928571   .0807107    -4.55   0.000      .262639    .5876382
------------------------------------------------------------------------------

. logistic prepos hipo

Logistic regression                             Number of obs     =        588
                                                LR chi2(1)        =       1.15
                                                Prob > chi2       =     0.2827
Log likelihood = -368.65264                     Pseudo R2         =     0.0016

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        hipo |        .75   .2040762    -1.06   0.290     .4399952    1.278423
       _cons |   .4912281   .0462808    -7.54   0.000     .4084019    .5908519
------------------------------------------------------------------------------

. logistic prepos endometr

Logistic regression                             Number of obs     =        588
                                                LR chi2(1)        =       2.25
                                                Prob > chi2       =     0.1335
Log likelihood = -368.10387                     Pseudo R2         =     0.0030

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    endometr |   1.970543   .8792091     1.52   0.128     .8218647    4.724672
       _cons |   .4613402   .0416841    -8.56   0.000     .3864662    .5507203
------------------------------------------------------------------------------

. logistic prepos obesidade

Logistic regression                             Number of obs     =        588
                                                LR chi2(1)        =       0.93
                                                Prob > chi2       =     0.3353
Log likelihood = -368.76552                     Pseudo R2         =     0.0013

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   obesidade |   1.383584   .4608687     0.97   0.330     .7202315    2.657901
       _cons |   .4625668   .0425314    -8.38   0.000     .3862865    .5539103
------------------------------------------------------------------------------

. logistic prepos has

Logistic regression                             Number of obs     =        588
                                                LR chi2(1)        =       1.22
                                                Prob > chi2       =     0.2697
Log likelihood = -368.62047                     Pseudo R2         =     0.0016

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         has |   1.236486   .2368353     1.11   0.268      .849476    1.799814
       _cons |   .4444444   .0472131    -7.63   0.000     .3609065    .5473187
------------------------------------------------------------------------------

. logistic prepos dm

Logistic regression                             Number of obs     =        588
                                                LR chi2(1)        =       0.34
                                                Prob > chi2       =     0.5604
Log likelihood = -369.06011                     Pseudo R2         =     0.0005

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          dm |   .8401163   .2537765    -0.58   0.564     .4647446    1.518674
       _cons |   .4817927   .0447188    -7.87   0.000     .4016557    .5779185
------------------------------------------------------------------------------

. logistic prepos dlp

Logistic regression                             Number of obs     =        588
                                                LR chi2(1)        =       2.19
                                                Prob > chi2       =     0.1392
Log likelihood = -368.13615                     Pseudo R2         =     0.0030

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         dlp |   .6865672   .1782958    -1.45   0.148     .4126987    1.142176
       _cons |         .5   .0475293    -7.29   0.000     .4150075    .6023988
------------------------------------------------------------------------------

. logistic prepos fogacho

Logistic regression                             Number of obs     =        588
                                                LR chi2(1)        =       0.19
                                                Prob > chi2       =     0.6618
Log likelihood =   -369.134                     Pseudo R2         =     0.0003

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     fogacho |   .9115084     .19372    -0.44   0.663     .6009765    1.382496
       _cons |   .4836601   .0484258    -7.25   0.000     .3974797     .588526
------------------------------------------------------------------------------

. logistic prepos sangr

Logistic regression                             Number of obs     =        588
                                                LR chi2(1)        =       0.01
                                                Prob > chi2       =     0.9308
Log likelihood = -369.22588                     Pseudo R2         =     0.0000

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       sangr |   1.019365   .2250039     0.09   0.931     .6613696     1.57114
       _cons |    .471875    .046588    -7.61   0.000     .3888555    .5726189
------------------------------------------------------------------------------

. logistic prepos diu

Logistic regression                             Number of obs     =        588
                                                LR chi2(1)        =       0.32
                                                Prob > chi2       =     0.5710
Log likelihood = -369.06914                     Pseudo R2         =     0.0004

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         diu |       1.28    .551942     0.57   0.567     .5497533    2.980246
       _cons |     .46875   .0423427    -8.39   0.000     .3926913    .5595402
------------------------------------------------------------------------------

. logistic prepos osteop

Logistic regression                             Number of obs     =        588
                                                LR chi2(1)        =       2.06
                                                Prob > chi2       =     0.1512
Log likelihood = -368.19936                     Pseudo R2         =     0.0028

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      osteop |   .7183451   .1682146    -1.41   0.158     .4539496    1.136733
       _cons |   .5031646   .0489232    -7.06   0.000     .4158597     .608798
------------------------------------------------------------------------------

. logistic prepos cardiop

Logistic regression                             Number of obs     =        588
                                                LR chi2(1)        =       0.30
                                                Prob > chi2       =     0.5842
Log likelihood = -369.07992                     Pseudo R2         =     0.0004

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     cardiop |   .6989247   .4700937    -0.53   0.594      .187032     2.61183
       _cons |   .4769231   .0424982    -8.31   0.000     .4004963    .5679344
------------------------------------------------------------------------------

. logistic prepos menop

Logistic regression                             Number of obs     =        588
                                                LR chi2(1)        =       0.59
                                                Prob > chi2       =     0.4440
Log likelihood =  -368.9367                     Pseudo R2         =     0.0008

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       menop |   .8723404   .1555152    -0.77   0.444     .6150909    1.237179
       _cons |   .5121951   .0687226    -4.99   0.000     .3937562    .6662596
------------------------------------------------------------------------------

. logistic prepos morbid
no observations
r(2000);

. logistic prepos idadecat

Logistic regression                             Number of obs     =        588
                                                LR chi2(1)        =       8.98
                                                Prob > chi2       =     0.0027
Log likelihood = -364.74212                     Pseudo R2         =     0.0122

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    idadecat |    .569465   .1065061    -3.01   0.003      .394701    .8216103
       _cons |   .6915888   .1045633    -2.44   0.015     .5142239    .9301299
------------------------------------------------------------------------------

. logistic prepos idadecat2

Logistic regression                             Number of obs     =        588
                                                LR chi2(1)        =       3.99
                                                Prob > chi2       =     0.0457
Log likelihood = -367.23342                     Pseudo R2         =     0.0054

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   idadecat2 |   .8458425   .0708208    -2.00   0.046     .7178273    .9966876
       _cons |   .7112183   .1560564    -1.55   0.120     .4626272    1.093389
------------------------------------------------------------------------------

. logistic prepos i.idadecat2

Logistic regression                             Number of obs     =        588
                                                LR chi2(4)        =       8.03
                                                Prob > chi2       =     0.0906
Log likelihood = -365.21675                     Pseudo R2         =     0.0109

-----------------------------------------------------------------------------------
           prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
        idadecat2 |
    35 a 44 anos  |       1.98    .898285     1.51   0.132     .8137531    4.817677
    45 a 54 anos  |   1.233871   .4668221     0.56   0.579     .5877959    2.590078
    55 a 64 anos  |   .9140625   .3444512    -0.24   0.812     .4367299    1.913105
Acima de 65 anos  |   .7857143   .3349189    -0.57   0.572     .3407463     1.81175
                  |
            _cons |   .4444444   .1541975    -2.34   0.019     .2251635    .8772776
-----------------------------------------------------------------------------------

. logistic prepos etnia

Logistic regression                             Number of obs     =        588
                                                LR chi2(1)        =       0.13
                                                Prob > chi2       =     0.7218
Log likelihood = -369.16624                     Pseudo R2         =     0.0002

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       etnia |      1.075   .2178762     0.36   0.721     .7225881    1.599286
       _cons |   .4651163    .047581    -7.48   0.000     .3806136    .5683799
------------------------------------------------------------------------------

. logistic prepos i.etnia

Logistic regression                             Number of obs     =        588
                                                LR chi2(1)        =       0.13
                                                Prob > chi2       =     0.7218
Log likelihood = -369.16624                     Pseudo R2         =     0.0002

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       etnia |
 Não branca  |      1.075   .2178762     0.36   0.721     .7225881    1.599286
       _cons |   .4651163    .047581    -7.48   0.000     .3806136    .5683799
------------------------------------------------------------------------------

. logistic prepos multimorbcat

Logistic regression                             Number of obs     =        588
                                                LR chi2(1)        =       0.62
                                                Prob > chi2       =     0.4312
Log likelihood = -368.91991                     Pseudo R2         =     0.0008

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
multimorbcat |   .8682681   .1561061    -0.79   0.432     .6104023     1.23507
       _cons |   .5022026   .0576489    -6.00   0.000     .4010216    .6289124
------------------------------------------------------------------------------

. logistic prepos dcid1

Logistic regression                             Number of obs     =        588
                                                LR chi2(1)        =       0.08
                                                Prob > chi2       =     0.7789
Log likelihood = -369.19024                     Pseudo R2         =     0.0001

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       dcid1 |      1.105   .3911119     0.28   0.778     .5521816    2.211276
       _cons |   .4705882    .043016    -8.25   0.000     .3933993    .5629225
------------------------------------------------------------------------------

. logistic prepos dcid2

Logistic regression                             Number of obs     =        588
                                                LR chi2(1)        =       0.63
                                                Prob > chi2       =     0.4277
Log likelihood = -368.91506                     Pseudo R2         =     0.0009

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       dcid2 |   1.496154   .7494556     0.80   0.421       .56052    3.993571
       _cons |   .4678663   .0420174    -8.46   0.000     .3923543    .5579113
------------------------------------------------------------------------------

. logistic prepos dcid3

Logistic regression                             Number of obs     =        588
                                                LR chi2(1)        =       2.66
                                                Prob > chi2       =     0.1028
Log likelihood = -367.89879                     Pseudo R2         =     0.0036

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       dcid3 |   2.519231   1.419655     1.64   0.101     .8348199    7.602267
       _cons |   .4631043   .0415222    -8.59   0.000     .3884718    .5520751
------------------------------------------------------------------------------

. logistic prepos dcid4

Logistic regression                             Number of obs     =        588
                                                LR chi2(1)        =       0.89
                                                Prob > chi2       =     0.3454
Log likelihood = -368.78448                     Pseudo R2         =     0.0012

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       dcid4 |   1.247059   .2947824     0.93   0.350     .7846538    1.981964
       _cons |   .3947368   .0851125    -4.31   0.000     .2586861    .6023408
------------------------------------------------------------------------------

. logistic prepos dcid5

Logistic regression                             Number of obs     =        588
                                                LR chi2(1)        =       3.40
                                                Prob > chi2       =     0.0652
Log likelihood = -367.52955                     Pseudo R2         =     0.0046

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       dcid5 |    .619403   .1656354    -1.79   0.073      .366735    1.046151
       _cons |   .5060241   .0479107    -7.19   0.000     .4203188    .6092051
------------------------------------------------------------------------------

. logistic idadecat dcid5

Logistic regression                             Number of obs     =        588
                                                LR chi2(1)        =       5.89
                                                Prob > chi2       =     0.0152
Log likelihood = -360.05416                     Pseudo R2         =     0.0081

------------------------------------------------------------------------------
    idadecat | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       dcid5 |   .5575463   .1323962    -2.46   0.014     .3500683    .8879922
       _cons |   2.472222   .2441554     9.16   0.000     2.037151     3.00021
------------------------------------------------------------------------------

. logistic idadecat dcid3

Logistic regression                             Number of obs     =        588
                                                LR chi2(1)        =       5.34
                                                Prob > chi2       =     0.0209
Log likelihood = -360.33121                     Pseudo R2         =     0.0074

------------------------------------------------------------------------------
    idadecat | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       dcid3 |   .2689681   .1552735    -2.27   0.023     .0867576    .8338615
       _cons |   2.323699   .2112894     9.27   0.000     1.944383    2.777014
------------------------------------------------------------------------------

. logistic idadecat endometr

Logistic regression                             Number of obs     =        588
                                                LR chi2(1)        =      11.88
                                                Prob > chi2       =     0.0006
Log likelihood = -357.05974                     Pseudo R2         =     0.0164

------------------------------------------------------------------------------
    idadecat | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    endometr |     .20875   .0985277    -3.32   0.001     .0827692     .526483
       _cons |    2.39521   .2206718     9.48   0.000     1.999502    2.869228
------------------------------------------------------------------------------

. logistic idadecat dlp

Logistic regression                             Number of obs     =        588
                                                LR chi2(1)        =       9.19
                                                Prob > chi2       =     0.0024
Log likelihood = -358.40702                     Pseudo R2         =     0.0127

------------------------------------------------------------------------------
    idadecat | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         dlp |   2.291667   .6684329     2.84   0.004     1.293818      4.0591
       _cons |   2.018182   .1921369     7.38   0.000     1.674647    2.432188
------------------------------------------------------------------------------

. logistic idadecat menop

Logistic regression                             Number of obs     =        588
                                                LR chi2(1)        =     127.23
                                                Prob > chi2       =     0.0000
Log likelihood = -299.38794                     Pseudo R2         =     0.1752

------------------------------------------------------------------------------
    idadecat | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       menop |   8.665116   1.796003    10.42   0.000      5.77228    13.00773
       _cons |   .7971014   .1018834    -1.77   0.076     .6204621    1.024028
------------------------------------------------------------------------------

. logistic idadecat fogacho

Logistic regression                             Number of obs     =        588
                                                LR chi2(1)        =       0.23
                                                Prob > chi2       =     0.6308
Log likelihood = -362.88502                     Pseudo R2         =     0.0003

------------------------------------------------------------------------------
    idadecat | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     fogacho |   1.108645   .2388474     0.48   0.632     .7267903    1.691127
       _cons |   2.197183   .2224194     7.78   0.000     1.801771    2.679371
------------------------------------------------------------------------------

. logistic idadecat dcid5 dcid3

Logistic regression                             Number of obs     =        588
                                                LR chi2(2)        =      12.07
                                                Prob > chi2       =     0.0024
Log likelihood = -356.96779                     Pseudo R2         =     0.0166

------------------------------------------------------------------------------
    idadecat | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       dcid5 |   .5340725   .1273254    -2.63   0.009     .3347108    .8521788
       dcid3 |   .2421656   .1402057    -2.45   0.014     .0778566    .7532331
       _cons |   2.580882   .2606812     9.39   0.000     2.117351     3.14589
------------------------------------------------------------------------------

. logistic idadecat dcid5 dcid3 endometr

Logistic regression                             Number of obs     =        588
                                                LR chi2(3)        =      25.26
                                                Prob > chi2       =     0.0000
Log likelihood = -350.37264                     Pseudo R2         =     0.0348

------------------------------------------------------------------------------
    idadecat | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       dcid5 |    .512185    .123279    -2.78   0.005     .3195576     .820927
       dcid3 |   .2242381   .1299728    -2.58   0.010     .0720006    .6983649
    endometr |   .1897973   .0901208    -3.50   0.000     .0748371    .4813524
       _cons |   2.787216   .2917773     9.79   0.000     2.270196    3.421984
------------------------------------------------------------------------------

. logistic idadecat dcid5 dcid3 endometr dlp

Logistic regression                             Number of obs     =        588
                                                LR chi2(4)        =      31.88
                                                Prob > chi2       =     0.0000
Log likelihood = -347.05904                     Pseudo R2         =     0.0439

------------------------------------------------------------------------------
    idadecat | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       dcid5 |    .555045   .1348511    -2.42   0.015     .3447659    .8935772
       dcid3 |     .25045    .145497    -2.38   0.017     .0802088     .782024
    endometr |   .1879296   .0900383    -3.49   0.000     .0734816    .4806308
         dlp |   2.072938   .6190636     2.44   0.015     1.154471    3.722114
       _cons |   2.495509   .2789415     8.18   0.000     2.004536    3.106735
------------------------------------------------------------------------------

. logistic prepos idadecat

Logistic regression                             Number of obs     =        588
                                                LR chi2(1)        =       8.98
                                                Prob > chi2       =     0.0027
Log likelihood = -364.74212                     Pseudo R2         =     0.0122

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    idadecat |    .569465   .1065061    -3.01   0.003      .394701    .8216103
       _cons |   .6915888   .1045633    -2.44   0.015     .5142239    .9301299
------------------------------------------------------------------------------

. logistic prepos idadecat dcid5

Logistic regression                             Number of obs     =        588
                                                LR chi2(2)        =      13.72
                                                Prob > chi2       =     0.0011
Log likelihood = -362.37161                     Pseudo R2         =     0.0186

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    idadecat |   .5431391   .1028428    -3.22   0.001      .374746    .7872003
       dcid5 |   .5643381   .1533487    -2.11   0.035     .3313146    .9612538
       _cons |   .7726444   .1235013    -1.61   0.107      .564835    1.056909
------------------------------------------------------------------------------

. logistic prepos idadecat dcid5 dcid3

Logistic regression                             Number of obs     =        588
                                                LR chi2(3)        =      15.10
                                                Prob > chi2       =     0.0017
Log likelihood = -361.67854                     Pseudo R2         =     0.0205

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    idadecat |   .5560332    .105992    -3.08   0.002     .3826866    .8079011
       dcid5 |   .5777145   .1573215    -2.01   0.044      .338779    .9851673
       dcid3 |   1.964654   1.126017     1.18   0.239     .6388964    6.041459
       _cons |    .745886   .1215052    -1.80   0.072      .542015     1.02644
------------------------------------------------------------------------------

. logistic prepos idadecat dcid5 endometr

Logistic regression                             Number of obs     =        588
                                                LR chi2(3)        =      14.64
                                                Prob > chi2       =     0.0022
Log likelihood = -361.91071                     Pseudo R2         =     0.0198

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    idadecat |    .558714   .1071373    -3.04   0.002      .383677    .8136046
       dcid5 |   .5711969    .155318    -2.06   0.039       .33522    .9732891
    endometr |   1.555999   .7117756     0.97   0.334     .6347988    3.814017
       _cons |   .7440925   .1226211    -1.79   0.073     .5387099    1.027777
------------------------------------------------------------------------------

. logistic prepos idadecat dcid5 dlp

Logistic regression                             Number of obs     =        588
                                                LR chi2(3)        =      15.64
                                                Prob > chi2       =     0.0013
Log likelihood = -361.40841                     Pseudo R2         =     0.0212

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    idadecat |   .5581553   .1063011    -3.06   0.002     .3842758     .810713
       dcid5 |   .5421573   .1481831    -2.24   0.025     .3173027    .9263536
         dlp |   .6972445   .1845086    -1.36   0.173     .4150836     1.17121
       _cons |   .8035139   .1305281    -1.35   0.178     .5844113    1.104761
------------------------------------------------------------------------------

. logistic prepos idadecat dcid5 fogacho

Logistic regression                             Number of obs     =        588
                                                LR chi2(3)        =      14.30
                                                Prob > chi2       =     0.0025
Log likelihood = -362.07719                     Pseudo R2         =     0.0194

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    idadecat |   .5426047    .102813    -3.23   0.001     .3742807    .7866284
       dcid5 |   .5451923   .1502506    -2.20   0.028     .3176621    .9356944
     fogacho |   .8468556   .1844339    -0.76   0.445      .552623    1.297746
       _cons |   .8064317   .1365528    -1.27   0.204     .5786739    1.123832
------------------------------------------------------------------------------

. logistic prepos idadecat dcid5 dcid4

Logistic regression                             Number of obs     =        588
                                                LR chi2(3)        =      13.72
                                                Prob > chi2       =     0.0033
Log likelihood =  -362.3716                     Pseudo R2         =     0.0186

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    idadecat |   .5431213   .1029533    -3.22   0.001     .3745798    .7874979
       dcid5 |   .5640726   .1694034    -1.91   0.057     .3131125    1.016178
       dcid4 |   .9990263   .2645027    -0.00   0.997     .5945829    1.678578
       _cons |   .7733338   .2245571    -0.89   0.376     .4377227    1.366265
------------------------------------------------------------------------------

. logistic prepos sangr

Logistic regression                             Number of obs     =        588
                                                LR chi2(1)        =       0.01
                                                Prob > chi2       =     0.9308
Log likelihood = -369.22588                     Pseudo R2         =     0.0000

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       sangr |   1.019365   .2250039     0.09   0.931     .6613696     1.57114
       _cons |    .471875    .046588    -7.61   0.000     .3888555    .5726189
------------------------------------------------------------------------------

. logistic prepos multimorbcat

Logistic regression                             Number of obs     =        588
                                                LR chi2(1)        =       0.62
                                                Prob > chi2       =     0.4312
Log likelihood = -368.91991                     Pseudo R2         =     0.0008

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
multimorbcat |   .8682681   .1561061    -0.79   0.432     .6104023     1.23507
       _cons |   .5022026   .0576489    -6.00   0.000     .4010216    .6289124
------------------------------------------------------------------------------

. logistic prepos idadecat dcid5 dcid3

Logistic regression                             Number of obs     =        588
                                                LR chi2(3)        =      15.10
                                                Prob > chi2       =     0.0017
Log likelihood = -361.67854                     Pseudo R2         =     0.0205

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    idadecat |   .5560332    .105992    -3.08   0.002     .3826866    .8079011
       dcid5 |   .5777145   .1573215    -2.01   0.044      .338779    .9851673
       dcid3 |   1.964654   1.126017     1.18   0.239     .6388964    6.041459
       _cons |    .745886   .1215052    -1.80   0.072      .542015     1.02644
------------------------------------------------------------------------------

. logistic prepos idadecat dcid5

Logistic regression                             Number of obs     =        588
                                                LR chi2(2)        =      13.72
                                                Prob > chi2       =     0.0011
Log likelihood = -362.37161                     Pseudo R2         =     0.0186

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    idadecat |   .5431391   .1028428    -3.22   0.001      .374746    .7872003
       dcid5 |   .5643381   .1533487    -2.11   0.035     .3313146    .9612538
       _cons |   .7726444   .1235013    -1.61   0.107      .564835    1.056909
------------------------------------------------------------------------------

. tab dcid1 prepos, row col chi exact

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

           |  Pré - Pós COVID-19
     CID-1 | Pré COVID  Pós COVID |     Total
-----------+----------------------+----------
       Não |       374        176 |       550 
           |     68.00      32.00 |    100.00 
           |     93.73      93.12 |     93.54 
-----------+----------------------+----------
       Sim |        25         13 |        38 
           |     65.79      34.21 |    100.00 
           |      6.27       6.88 |      6.46 
-----------+----------------------+----------
     Total |       399        189 |       588 
           |     67.86      32.14 |    100.00 
           |    100.00     100.00 |    100.00 

          Pearson chi2(1) =   0.0796   Pr = 0.778
           Fisher's exact =                 0.858
   1-sided Fisher's exact =                 0.452

. save "C:\Users\LIM58\Dropbox\Ginecologia e Obstetrícia\Isabel - Atenção primária a saúde\atendimentos.dta", replace
file C:\Users\LIM58\Dropbox\Ginecologia e Obstetrícia\Isabel - Atenção primária a saúde\atendimentos.dta saved

. exit, clear
-----------------------------------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  C:\Users\LIM58\Dropbox\Ginecologia e Obstetrícia\Isabel - Atenção primária a saúde\atend.log
  log type:  text
 opened on:  28 Jul 2021, 14:59:46

. bysort prepos: sum idade, d

-----------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pré COVID-19

                      Idade na consulta
-------------------------------------------------------------
      Percentiles      Smallest
 1%           22             12
 5%           36             14
10%           43             16       Obs               1,236
25%           49             17       Sum of Wgt.       1,236

50%           55                      Mean           54.20388
                        Largest       Std. Dev.       10.1559
75%           60             81
90%           66             82       Variance       103.1422
95%           70             82       Skewness       -.602543
99%           78             90       Kurtosis       4.699018

-----------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pós COVID-19

                      Idade na consulta
-------------------------------------------------------------
      Percentiles      Smallest
 1%           24             13
 5%           36             17
10%           41             21       Obs                 530
25%           49             22       Sum of Wgt.         530

50%           53                      Mean           53.01698
                        Largest       Std. Dev.      9.487316
75%           58             77
90%           64             79       Variance       90.00916
95%           68             81       Skewness      -.4280968
99%           75             92       Kurtosis       5.016252


. help shapiro

. swilk idade

                   Shapiro-Wilk W test for normal data

    Variable |        Obs       W           V         z       Prob>z
-------------+------------------------------------------------------
       idade |      1,766    0.96871     33.141     8.864    0.00000

. histogram idade, n
(bin=32, start=12, width=2.5)
option n not allowed
r(198);

. histogram idade, normal
(bin=32, start=12, width=2.5)

. qnorm idade

. save "C:\Users\LIM58\Dropbox\Ginecologia e Obstetrícia\Isabel - Atenção primária a saúde\atendimentos.dta", replace
file C:\Users\LIM58\Dropbox\Ginecologia e Obstetrícia\Isabel - Atenção primária a saúde\atendimentos.dta saved

. ttest idade prepos
too many variables specified
r(103);

. ttest idade
by() option required
r(100);

. ttest idade, by (prepos)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
Pré COVI |   1,236    54.20388    .2888743     10.1559    53.63714    54.77062
Pós COVI |     530    53.01698    .4121027    9.487316    52.20742    53.82654
---------+--------------------------------------------------------------------
combined |   1,766    53.84768    .2372975    9.972143    53.38226    54.31309
---------+--------------------------------------------------------------------
    diff |            1.186902     .517145                .1726209    2.201184
------------------------------------------------------------------------------
    diff = mean(Pré COVI) - mean(Pós COVI)                        t =   2.2951
Ho: diff = 0                                     degrees of freedom =     1764

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.9891         Pr(|T| > |t|) = 0.0218          Pr(T > t) = 0.0109

. ranksum idade,by(prepos)

Two-sample Wilcoxon rank-sum (Mann-Whitney) test

      prepos |      obs    rank sum    expected
-------------+---------------------------------
 Pré COVID-1 |     1236     1121292     1092006
 Pós COVID-1 |      530      438969      468255
-------------+---------------------------------
    combined |     1766     1560261     1560261

unadjusted variance    96460530
adjustment for ties  -139895.86
                     ----------
adjusted variance      96320634

Ho: idade(prepos==Pré COVID-19) = idade(prepos==Pós COVID-19)
             z =   2.984
    Prob > |z| =   0.0028

. bysort prepos: sum peso, d

-----------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pré COVID-19

                            Peso
-------------------------------------------------------------
      Percentiles      Smallest
 1%           46           44.3
 5%           52           45.7
10%           55             46       Obs                 251
25%         61.2             46       Sum of Wgt.         251

50%           72                      Mean           73.13398
                        Largest       Std. Dev.      14.85592
75%           83         107.85
90%           90            114       Variance       220.6984
95%        100.8          128.4       Skewness       .7102294
99%          114            132       Kurtosis       3.975334

-----------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pós COVID-19

                            Peso
-------------------------------------------------------------
      Percentiles      Smallest
 1%           52             52
 5%         53.5           52.4
10%        58.15             53       Obs                  76
25%           64           53.5       Sum of Wgt.          76

50%         72.7                      Mean           73.87289
                        Largest       Std. Dev.      15.22402
75%           81         104.25
90%           87            106       Variance       231.7708
95%       104.25         113.65       Skewness        1.59832
99%        143.5          143.5       Kurtosis       7.687578


. ranksum peso,by(prepos)

Two-sample Wilcoxon rank-sum (Mann-Whitney) test

      prepos |      obs    rank sum    expected
-------------+---------------------------------
 Pré COVID-1 |      251     41026.5       41164
 Pós COVID-1 |       76     12601.5       12464
-------------+---------------------------------
    combined |      327       53628       53628

unadjusted variance   521410.67
adjustment for ties      -45.54
                     ----------
adjusted variance     521365.12

Ho: peso(prepos==Pré COVID-19) = peso(prepos==Pós COVID-19)
             z =  -0.190
    Prob > |z| =   0.8490

. bysort prepos: sum altura, d

-----------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pré COVID-19

                           Altura
-------------------------------------------------------------
      Percentiles      Smallest
 1%         1.42           1.35
 5%         1.46           1.37
10%         1.49           1.42       Obs                 241
25%         1.53           1.43       Sum of Wgt.         241

50%         1.58                      Mean           1.580083
                        Largest       Std. Dev.      .0717809
75%         1.62           1.79
90%         1.67            1.8       Variance       .0051525
95%         1.69            1.8       Skewness       .1044496
99%          1.8            1.8       Kurtosis        3.78484

-----------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pós COVID-19

                           Altura
-------------------------------------------------------------
      Percentiles      Smallest
 1%         1.45           1.45
 5%         1.49           1.46
10%         1.51           1.48       Obs                  74
25%         1.53           1.49       Sum of Wgt.          74

50%         1.58                      Mean           1.591959
                        Largest       Std. Dev.      .0704624
75%         1.65            1.7
90%         1.69           1.71       Variance       .0049649
95%          1.7           1.77       Skewness       .3303433
99%         1.77           1.77       Kurtosis       2.620845


. ranksum altura,by(prepos)

Two-sample Wilcoxon rank-sum (Mann-Whitney) test

      prepos |      obs    rank sum    expected
-------------+---------------------------------
 Pré COVID-1 |      241       37385       38078
 Pós COVID-1 |       74       12385       11692
-------------+---------------------------------
    combined |      315       49770       49770

unadjusted variance   469628.67
adjustment for ties    -1240.95
                     ----------
adjusted variance     468387.72

Ho: altura(prepos==Pré COVID-19) = altura(prepos==Pós COVID-19)
             z =  -1.013
    Prob > |z| =   0.3113

. bysort prepos: sum imc, d

-----------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pré COVID-19

                             IMC
-------------------------------------------------------------
      Percentiles      Smallest
 1%     19.26969       16.12457
 5%     21.77843       18.66201
10%     22.90712       19.26969       Obs                 241
25%     25.40608       20.47902       Sum of Wgt.         241

50%     28.53333                      Mean           29.23446
                        Largest       Std. Dev.      5.255681
75%     32.79917       43.11111
90%     36.13945       44.72771       Variance       27.62218
95%     38.29467       46.68023       Skewness       .6336179
99%     44.72771       47.55556       Kurtosis       3.632929

-----------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pós COVID-19

                             IMC
-------------------------------------------------------------
      Percentiles      Smallest
 1%     19.81405       19.81405
 5%     21.61281       20.63964
10%     22.31328       21.42782       Obs                  74
25%     25.03992       21.61281       Sum of Wgt.          74

50%     28.93494                      Mean           29.32326
                        Largest       Std. Dev.      5.496291
75%     32.76517        37.9069
90%     36.27629       40.72266       Variance       30.20921
95%      37.9069       44.12071       Skewness       .5856656
99%     45.80421       45.80421       Kurtosis       3.190495


. ranksum imc,by(prepos)

Two-sample Wilcoxon rank-sum (Mann-Whitney) test

      prepos |      obs    rank sum    expected
-------------+---------------------------------
 Pré COVID-1 |      241     38028.5       38078
 Pós COVID-1 |       74     11741.5       11692
-------------+---------------------------------
    combined |      315       49770       49770

unadjusted variance   469628.67
adjustment for ties       -2.07
                     ----------
adjusted variance     469626.59

Ho: imc(prepos==Pré COVID-19) = imc(prepos==Pós COVID-19)
             z =  -0.072
    Prob > |z| =   0.9424

. bysort prepos: sum pas, d

-----------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pré COVID-19

                             PAS
-------------------------------------------------------------
      Percentiles      Smallest
 1%           88             83
 5%           95             85
10%          100             88       Obs                 234
25%          108             90       Sum of Wgt.         234

50%          119                      Mean           120.7179
                        Largest       Std. Dev.      18.27113
75%          130            179
90%          146            180       Variance       333.8343
95%          152            182       Skewness       .9313962
99%          180            196       Kurtosis       4.603587

-----------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pós COVID-19

                             PAS
-------------------------------------------------------------
      Percentiles      Smallest
 1%           95             95
 5%          100             95
10%          104             98       Obs                  63
25%          114            100       Sum of Wgt.          63

50%          123                      Mean           126.2381
                        Largest       Std. Dev.      17.36581
75%          139            155
90%          150            155       Variance       301.5714
95%          155            162       Skewness       .4755252
99%          180            180       Kurtosis       3.282737


. ranksum pas,by(prepos)

Two-sample Wilcoxon rank-sum (Mann-Whitney) test

      prepos |      obs    rank sum    expected
-------------+---------------------------------
 Pré COVID-1 |      234     33334.5       34866
 Pós COVID-1 |       63     10918.5        9387
-------------+---------------------------------
    combined |      297       44253       44253

unadjusted variance   366093.00
adjustment for ties     -538.20
                     ----------
adjusted variance     365554.80

Ho: pas(prepos==Pré COVID-19) = pas(prepos==Pós COVID-19)
             z =  -2.533
    Prob > |z| =   0.0113

. bysort prepos: sum pad, d

-----------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pré COVID-19

                             PAD
-------------------------------------------------------------
      Percentiles      Smallest
 1%           48             47
 5%           55             48
10%         58.5             48       Obs                 200
25%           65             50       Sum of Wgt.         200

50%           71                      Mean               72.2
                        Largest       Std. Dev.      11.03398
75%           80            100
90%         86.5            100       Variance       121.7487
95%           91            102       Skewness       .3420193
99%          101            106       Kurtosis       3.214713

-----------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pós COVID-19

                             PAD
-------------------------------------------------------------
      Percentiles      Smallest
 1%           50             50
 5%           60             58
10%           64             60       Obs                  57
25%           70             60       Sum of Wgt.          57

50%           80                      Mean           77.52632
                        Largest       Std. Dev.      10.61519
75%           83             93
90%           90             97       Variance       112.6823
95%           97            100       Skewness       -.110971
99%          100            100       Kurtosis        2.85328


. ranksum pad,by(prepos)

Two-sample Wilcoxon rank-sum (Mann-Whitney) test

      prepos |      obs    rank sum    expected
-------------+---------------------------------
 Pré COVID-1 |      200     24116.5       25800
 Pós COVID-1 |       57      9036.5        7353
-------------+---------------------------------
    combined |      257       33153       33153

unadjusted variance   245100.00
adjustment for ties     -515.06
                     ----------
adjusted variance     244584.94

Ho: pad(prepos==Pré COVID-19) = pad(prepos==Pós COVID-19)
             z =  -3.404
    Prob > |z| =   0.0007

. bysort prepos: sum idademenopausa , d

-----------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pré COVID-19

                     Idade da menopausa
-------------------------------------------------------------
      Percentiles      Smallest
 1%           22             18
 5%           33             18
10%           37             20       Obs                 502
25%           43             20       Sum of Wgt.         502

50%           48                      Mean           46.37849
                        Largest       Std. Dev.      7.071645
75%           51             59
90%           54             59       Variance       50.00816
95%           55             63       Skewness      -1.138262
99%           59             67       Kurtosis       5.075573

-----------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pós COVID-19

                     Idade da menopausa
-------------------------------------------------------------
      Percentiles      Smallest
 1%           23             18
 5%           33             18
10%           38             23       Obs                 240
25%           43             26       Sum of Wgt.         240

50%           48                      Mean           46.79583
                        Largest       Std. Dev.      7.009556
75%           51             62
90%           54             62       Variance       49.13387
95%           55             63       Skewness      -1.016786
99%           62             64       Kurtosis       5.401231


. ranksum idademenopausa ,by(prepos)

Two-sample Wilcoxon rank-sum (Mann-Whitney) test

      prepos |      obs    rank sum    expected
-------------+---------------------------------
 Pré COVID-1 |      502      185214      186493
 Pós COVID-1 |      240       90439       89160
-------------+---------------------------------
    combined |      742      275653      275653

unadjusted variance  7459720.00
adjustment for ties   -28746.52
                     ----------
adjusted variance    7430973.48

Ho: idadem~a(prepos==Pré COVID-19) = idadem~a(prepos==Pós COVID-19)
             z =  -0.469
    Prob > |z| =   0.6389

. bysort prepos: sum paridade , d

-----------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pré COVID-19

                   Número de gestações
-------------------------------------------------------------
      Percentiles      Smallest
 1%            1              1
 5%            1              1
10%            1              1       Obs                 309
25%            2              1       Sum of Wgt.         309

50%            3                      Mean           2.721683
                        Largest       Std. Dev.      1.505471
75%            3              7
90%            5              8       Variance       2.266444
95%            5             10       Skewness       1.280493
99%            7             10       Kurtosis       6.038918

-----------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pós COVID-19

                   Número de gestações
-------------------------------------------------------------
      Percentiles      Smallest
 1%            1              1
 5%            1              1
10%            1              1       Obs                 213
25%            2              1       Sum of Wgt.         213

50%            3                      Mean           2.821596
                        Largest       Std. Dev.      1.340974
75%            4              6
90%            5              6       Variance       1.798211
95%            5              7       Skewness       .7161552
99%            6              8       Kurtosis       3.499396


. ranksum paridade ,by(prepos)

Two-sample Wilcoxon rank-sum (Mann-Whitney) test

      prepos |      obs    rank sum    expected
-------------+---------------------------------
 Pré COVID-1 |      309     78712.5     80803.5
 Pós COVID-1 |      213     57790.5     55699.5
-------------+---------------------------------
    combined |      522      136503      136503

unadjusted variance  2868524.25
adjustment for ties  -146638.14
                     ----------
adjusted variance    2721886.11

Ho: paridade(prepos==Pré COVID-19) = paridade(prepos==Pós COVID-19)
             z =  -1.267
    Prob > |z| =   0.2050

. bysort prepos: sum menarca , d

-----------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pré COVID-19

                      Idade da menarca
-------------------------------------------------------------
      Percentiles      Smallest
 1%            9              8
 5%           10              9
10%           11              9       Obs                 176
25%           12              9       Sum of Wgt.         176

50%           13                      Mean           13.15909
                        Largest       Std. Dev.       2.54058
75%           14             18
90%           15             21       Variance       6.454545
95%           17             28       Skewness       2.448402
99%           28             28       Kurtosis       14.83456

-----------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pós COVID-19

                      Idade da menarca
-------------------------------------------------------------
      Percentiles      Smallest
 1%            8              8
 5%           10              8
10%           11              9       Obs                 128
25%           12             10       Sum of Wgt.         128

50%           13                      Mean           13.14063
                        Largest       Std. Dev.      2.030222
75%           14             17
90%           16             17       Variance       4.121801
95%           17             18       Skewness       .1537936
99%           18             18       Kurtosis       2.791631


. ranksum menarca ,by(prepos)

Two-sample Wilcoxon rank-sum (Mann-Whitney) test

      prepos |      obs    rank sum    expected
-------------+---------------------------------
 Pré COVID-1 |      176     26392.5       26840
 Pós COVID-1 |      128     19967.5       19520
-------------+---------------------------------
    combined |      304       46360       46360

unadjusted variance   572586.67
adjustment for ties   -14761.87
                     ----------
adjusted variance     557824.80

Ho: menarca(prepos==Pré COVID-19) = menarca(prepos==Pós COVID-19)
             z =  -0.599
    Prob > |z| =   0.5491

. bysort prepos: sum coitarca , d

-----------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pré COVID-19

                      Idade da coitarca
-------------------------------------------------------------
      Percentiles      Smallest
 1%           13             13
 5%           14             14
10%           15             15       Obs                  21
25%           16             15       Sum of Wgt.          21

50%           17                      Mean           20.61905
                        Largest       Std. Dev.      7.826086
75%           24             32
90%           32             32       Variance       61.24762
95%           38             38       Skewness       1.259692
99%           38             38       Kurtosis       3.175121

-----------------------------------------------------------------------------------------------------------------------------------------------
-> prepos = Pós COVID-19

                      Idade da coitarca
-------------------------------------------------------------
      Percentiles      Smallest
 1%           12             12
 5%           14             13
10%           15             14       Obs                  41
25%           16             15       Sum of Wgt.          41

50%           17                      Mean           19.02439
                        Largest       Std. Dev.      5.497671
75%           19             27
90%           25             27       Variance       30.22439
95%           27             38       Skewness       2.127121
99%           38             38       Kurtosis       7.746412


. ranksum coitarca ,by(prepos)

Two-sample Wilcoxon rank-sum (Mann-Whitney) test

      prepos |      obs    rank sum    expected
-------------+---------------------------------
 Pré COVID-1 |       21       648.5       661.5
 Pós COVID-1 |       41      1304.5      1291.5
-------------+---------------------------------
    combined |       62        1953        1953

unadjusted variance     4520.25
adjustment for ties      -72.28
                     ----------
adjusted variance       4447.97

Ho: coitarca(prepos==Pré COVID-19) = coitarca(prepos==Pós COVID-19)
             z =  -0.195
    Prob > |z| =   0.8455

. tab multimorb

Multimorbid |
        ade |      Freq.     Percent        Cum.
------------+-----------------------------------
        Não |      1,303       73.78       73.78
        Sim |        408       23.10       96.89
          2 |         51        2.89       99.77
          3 |          4        0.23      100.00
------------+-----------------------------------
      Total |      1,766      100.00

. recode multimorb 0=0 1=1
(multimorb: 0 changes made)

. tab multimorb

Multimorbid |
        ade |      Freq.     Percent        Cum.
------------+-----------------------------------
        Não |      1,303       73.78       73.78
        Sim |        408       23.10       96.89
          2 |         51        2.89       99.77
          3 |          4        0.23      100.00
------------+-----------------------------------
      Total |      1,766      100.00

. label define multimorb 0 "0" 1"1" 2"2" 3"3"

. label val multimorb multimorb

. tab multimorb

Multimorbid |
        ade |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      1,303       73.78       73.78
          1 |        408       23.10       96.89
          2 |         51        2.89       99.77
          3 |          4        0.23      100.00
------------+-----------------------------------
      Total |      1,766      100.00

. gen multimorbcat = multimorb

. save "C:\Users\LIM58\Dropbox\Ginecologia e Obstetrícia\Isabel - Atenção primária a saúde\atendimentos.dta", replace
file C:\Users\LIM58\Dropbox\Ginecologia e Obstetrícia\Isabel - Atenção primária a saúde\atendimentos.dta saved

. exit, clear
---------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  C:\Users\User\Dropbox\Ginecologia e Obstetrícia\Isabel - Atenção primária a saúde\atend.log
  log type:  text
 opened on:  30 Jul 2021, 08:13:05

. tab multimorbcat prepos, row col chi

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

multimorbc |  Pré - Pós COVID-19
        at | Pré COVID  Pós COVID |     Total
-----------+----------------------+----------
         0 |       920        383 |     1,303 
           |     70.61      29.39 |    100.00 
           |     74.43      72.26 |     73.78 
-----------+----------------------+----------
         1 |       277        131 |       408 
           |     67.89      32.11 |    100.00 
           |     22.41      24.72 |     23.10 
-----------+----------------------+----------
         2 |        36         15 |        51 
           |     70.59      29.41 |    100.00 
           |      2.91       2.83 |      2.89 
-----------+----------------------+----------
         3 |         3          1 |         4 
           |     75.00      25.00 |    100.00 
           |      0.24       0.19 |      0.23 
-----------+----------------------+----------
     Total |     1,236        530 |     1,766 
           |     69.99      30.01 |    100.00 
           |    100.00     100.00 |    100.00 

          Pearson chi2(3) =   1.1470   Pr = 0.766

. recode multimorbcat min/1=0 2/max=1
(multimorbcat: 463 changes made)

. tab multimorbcat

multimorbca |
          t |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      1,711       96.89       96.89
          1 |         55        3.11      100.00
------------+-----------------------------------
      Total |      1,766      100.00

. tab multimorbcat prepos, row col chi exact

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

multimorbc |  Pré - Pós COVID-19
        at | Pré COVID  Pós COVID |     Total
-----------+----------------------+----------
         0 |     1,197        514 |     1,711 
           |     69.96      30.04 |    100.00 
           |     96.84      96.98 |     96.89 
-----------+----------------------+----------
         1 |        39         16 |        55 
           |     70.91      29.09 |    100.00 
           |      3.16       3.02 |      3.11 
-----------+----------------------+----------
     Total |     1,236        530 |     1,766 
           |     69.99      30.01 |    100.00 
           |    100.00     100.00 |    100.00 

          Pearson chi2(1) =   0.0229   Pr = 0.880
           Fisher's exact =                 1.000
   1-sided Fisher's exact =                 0.507

. bysort prepos: tab multimorbcat

---------------------------------------------------------------------------------------------------------------------
-> prepos = Pré COVID-19

multimorbca |
          t |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      1,197       96.84       96.84
          1 |         39        3.16      100.00
------------+-----------------------------------
      Total |      1,236      100.00

---------------------------------------------------------------------------------------------------------------------
-> prepos = Pós COVID-19

multimorbca |
          t |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        514       96.98       96.98
          1 |         16        3.02      100.00
------------+-----------------------------------
      Total |        530      100.00


. bysort prepos: tab menopausa

---------------------------------------------------------------------------------------------------------------------
-> prepos = Pré COVID-19

  Menopausa |      Freq.     Percent        Cum.
------------+-----------------------------------
        Não |        650       52.59       52.59
        Sim |        586       47.41      100.00
------------+-----------------------------------
      Total |      1,236      100.00

---------------------------------------------------------------------------------------------------------------------
-> prepos = Pós COVID-19

  Menopausa |      Freq.     Percent        Cum.
------------+-----------------------------------
        Não |        264       49.81       49.81
        Sim |        266       50.19      100.00
------------+-----------------------------------
      Total |        530      100.00


. tab menopausa prepos, row col chi exact

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

           |  Pré - Pós COVID-19
 Menopausa | Pré COVID  Pós COVID |     Total
-----------+----------------------+----------
       Não |       650        264 |       914 
           |     71.12      28.88 |    100.00 
           |     52.59      49.81 |     51.76 
-----------+----------------------+----------
       Sim |       586        266 |       852 
           |     68.78      31.22 |    100.00 
           |     47.41      50.19 |     48.24 
-----------+----------------------+----------
     Total |     1,236        530 |     1,766 
           |     69.99      30.01 |    100.00 
           |    100.00     100.00 |    100.00 

          Pearson chi2(1) =   1.1462   Pr = 0.284
           Fisher's exact =                 0.299
   1-sided Fisher's exact =                 0.154

. tab vidasexual prepos, row col chi exact

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

      Vida |
    sexual |
ativa/inat |  Pré - Pós COVID-19
       iva | Pré COVID  Pós COVID |     Total
-----------+----------------------+----------
       Não |       220         94 |       314 
           |     70.06      29.94 |    100.00 
           |     39.43      35.21 |     38.06 
-----------+----------------------+----------
       Sim |       338        173 |       511 
           |     66.14      33.86 |    100.00 
           |     60.57      64.79 |     61.94 
-----------+----------------------+----------
     Total |       558        267 |       825 
           |     67.64      32.36 |    100.00 
           |    100.00     100.00 |    100.00 

          Pearson chi2(1) =   1.3645   Pr = 0.243
           Fisher's exact =                 0.251
   1-sided Fisher's exact =                 0.137

. tab obesidade prepos, row col chi exact

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

           |  Pré - Pós COVID-19
 Obesidade | Pré COVID  Pós COVID |     Total
-----------+----------------------+----------
       Não |         7          6 |        13 
           |     53.85      46.15 |    100.00 
           |      0.57       1.13 |      0.74 
-----------+----------------------+----------
       Sim |     1,229        524 |     1,753 
           |     70.11      29.89 |    100.00 
           |     99.43      98.87 |     99.26 
-----------+----------------------+----------
     Total |     1,236        530 |     1,766 
           |     69.99      30.01 |    100.00 
           |    100.00     100.00 |    100.00 

          Pearson chi2(1) =   1.6247   Pr = 0.202
           Fisher's exact =                 0.228
   1-sided Fisher's exact =                 0.165

. tab fogacho prepos, row col chi exact

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

           |  Pré - Pós COVID-19
  Fogachos | Pré COVID  Pós COVID |     Total
-----------+----------------------+----------
       Não |       798        305 |     1,103 
           |     72.35      27.65 |    100.00 
           |     64.56      57.55 |     62.46 
-----------+----------------------+----------
       Sim |       438        225 |       663 
           |     66.06      33.94 |    100.00 
           |     35.44      42.45 |     37.54 
-----------+----------------------+----------
     Total |     1,236        530 |     1,766 
           |     69.99      30.01 |    100.00 
           |    100.00     100.00 |    100.00 

          Pearson chi2(1) =   7.7869   Pr = 0.005
           Fisher's exact =                 0.006
   1-sided Fisher's exact =                 0.003

. tab sangr prepos, row col chi exact

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

Sangrament |  Pré - Pós COVID-19
         o | Pré COVID  Pós COVID |     Total
-----------+----------------------+----------
       Não |     1,010        382 |     1,392 
           |     72.56      27.44 |    100.00 
           |     81.72      72.08 |     78.82 
-----------+----------------------+----------
       Sim |       226        148 |       374 
           |     60.43      39.57 |    100.00 
           |     18.28      27.92 |     21.18 
-----------+----------------------+----------
     Total |     1,236        530 |     1,766 
           |     69.99      30.01 |    100.00 
           |    100.00     100.00 |    100.00 

          Pearson chi2(1) =  20.6493   Pr = 0.000
           Fisher's exact =                 0.000
   1-sided Fisher's exact =                 0.000

. tab diu prepos, row col chi exact

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

           |  Pré - Pós COVID-19
       DIU | Pré COVID  Pós COVID |     Total
-----------+----------------------+----------
       Não |     1,195        502 |     1,697 
           |     70.42      29.58 |    100.00 
           |     96.68      94.72 |     96.09 
-----------+----------------------+----------
       Sim |        41         28 |        69 
           |     59.42      40.58 |    100.00 
           |      3.32       5.28 |      3.91 
-----------+----------------------+----------
     Total |     1,236        530 |     1,766 
           |     69.99      30.01 |    100.00 
           |    100.00     100.00 |    100.00 

          Pearson chi2(1) =   3.8182   Pr = 0.051
           Fisher's exact =                 0.060
   1-sided Fisher's exact =                 0.037

. tab endometr prepos, row col chi exact

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

Endometrio |  Pré - Pós COVID-19
        se | Pré COVID  Pós COVID |     Total
-----------+----------------------+----------
       Não |     1,166        480 |     1,646 
           |     70.84      29.16 |    100.00 
           |     94.34      90.57 |     93.20 
-----------+----------------------+----------
       Sim |        70         50 |       120 
           |     58.33      41.67 |    100.00 
           |      5.66       9.43 |      6.80 
-----------+----------------------+----------
     Total |     1,236        530 |     1,766 
           |     69.99      30.01 |    100.00 
           |    100.00     100.00 |    100.00 

          Pearson chi2(1) =   8.3268   Pr = 0.004
           Fisher's exact =                 0.005
   1-sided Fisher's exact =                 0.003

. tab has prepos, row col chi exact

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

           |  Pré - Pós COVID-19
       HAS | Pré COVID  Pós COVID |     Total
-----------+----------------------+----------
       Não |       887        345 |     1,232 
           |     72.00      28.00 |    100.00 
           |     71.76      65.09 |     69.76 
-----------+----------------------+----------
       Sim |       349        185 |       534 
           |     65.36      34.64 |    100.00 
           |     28.24      34.91 |     30.24 
-----------+----------------------+----------
     Total |     1,236        530 |     1,766 
           |     69.99      30.01 |    100.00 
           |    100.00     100.00 |    100.00 

          Pearson chi2(1) =   7.8218   Pr = 0.005
           Fisher's exact =                 0.006
   1-sided Fisher's exact =                 0.003

. tab dm prepos, row col chi exact

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

           |  Pré - Pós COVID-19
      DMT2 | Pré COVID  Pós COVID |     Total
-----------+----------------------+----------
       Não |     1,081        461 |     1,542 
           |     70.10      29.90 |    100.00 
           |     87.46      86.98 |     87.32 
-----------+----------------------+----------
       Sim |       155         69 |       224 
           |     69.20      30.80 |    100.00 
           |     12.54      13.02 |     12.68 
-----------+----------------------+----------
     Total |     1,236        530 |     1,766 
           |     69.99      30.01 |    100.00 
           |    100.00     100.00 |    100.00 

          Pearson chi2(1) =   0.0767   Pr = 0.782
           Fisher's exact =                 0.815
   1-sided Fisher's exact =                 0.418

. tab dlp prepos, row col chi exact

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

           |  Pré - Pós COVID-19
       DLP | Pré COVID  Pós COVID |     Total
-----------+----------------------+----------
       Não |     1,042        441 |     1,483 
           |     70.26      29.74 |    100.00 
           |     84.30      83.21 |     83.98 
-----------+----------------------+----------
       Sim |       194         89 |       283 
           |     68.55      31.45 |    100.00 
           |     15.70      16.79 |     16.02 
-----------+----------------------+----------
     Total |     1,236        530 |     1,766 
           |     69.99      30.01 |    100.00 
           |    100.00     100.00 |    100.00 

          Pearson chi2(1) =   0.3315   Pr = 0.565
           Fisher's exact =                 0.572
   1-sided Fisher's exact =                 0.305

. tab osteop prepos, row col chi exact

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

Osteoporos |  Pré - Pós COVID-19
         e | Pré COVID  Pós COVID |     Total
-----------+----------------------+----------
       Não |       995        419 |     1,414 
           |     70.37      29.63 |    100.00 
           |     80.50      79.06 |     80.07 
-----------+----------------------+----------
       Sim |       241        111 |       352 
           |     68.47      31.53 |    100.00 
           |     19.50      20.94 |     19.93 
-----------+----------------------+----------
     Total |     1,236        530 |     1,766 
           |     69.99      30.01 |    100.00 
           |    100.00     100.00 |    100.00 

          Pearson chi2(1) =   0.4853   Pr = 0.486
           Fisher's exact =                 0.516
   1-sided Fisher's exact =                 0.263

. tab tabag prepos, row col chi exact

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

           |  Pré - Pós COVID-19
 Tabagismo | Pré COVID  Pós COVID |     Total
-----------+----------------------+----------
       Não |     1,175        502 |     1,677 
           |     70.07      29.93 |    100.00 
           |     95.06      94.72 |     94.96 
-----------+----------------------+----------
       Sim |        61         28 |        89 
           |     68.54      31.46 |    100.00 
           |      4.94       5.28 |      5.04 
-----------+----------------------+----------
     Total |     1,236        530 |     1,766 
           |     69.99      30.01 |    100.00 
           |    100.00     100.00 |    100.00 

          Pearson chi2(1) =   0.0937   Pr = 0.759
           Fisher's exact =                 0.812
   1-sided Fisher's exact =                 0.420

. tab cardiop prepos, row col chi exact

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

Cardiopati |  Pré - Pós COVID-19
         a | Pré COVID  Pós COVID |     Total
-----------+----------------------+----------
       Não |     1,190        516 |     1,706 
           |     69.75      30.25 |    100.00 
           |     96.28      97.36 |     96.60 
-----------+----------------------+----------
       Sim |        46         14 |        60 
           |     76.67      23.33 |    100.00 
           |      3.72       2.64 |      3.40 
-----------+----------------------+----------
     Total |     1,236        530 |     1,766 
           |     69.99      30.01 |    100.00 
           |    100.00     100.00 |    100.00 

          Pearson chi2(1) =   1.3187   Pr = 0.251
           Fisher's exact =                 0.316
   1-sided Fisher's exact =                 0.157

. tab etilism prepos, row col chi exact

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

           |  Pré - Pós COVID-19
  Etilismo | Pré COVID  Pós COVID |     Total
-----------+----------------------+----------
       Não |     1,226        519 |     1,745 
           |     70.26      29.74 |    100.00 
           |     99.19      97.92 |     98.81 
-----------+----------------------+----------
       Sim |        10         11 |        21 
           |     47.62      52.38 |    100.00 
           |      0.81       2.08 |      1.19 
-----------+----------------------+----------
     Total |     1,236        530 |     1,766 
           |     69.99      30.01 |    100.00 
           |    100.00     100.00 |    100.00 

          Pearson chi2(1) =   5.0631   Pr = 0.024
           Fisher's exact =                 0.031
   1-sided Fisher's exact =                 0.026

. tab multimorbcat prepos, row col chi exact

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

multimorbc |  Pré - Pós COVID-19
        at | Pré COVID  Pós COVID |     Total
-----------+----------------------+----------
         0 |     1,197        514 |     1,711 
           |     69.96      30.04 |    100.00 
           |     96.84      96.98 |     96.89 
-----------+----------------------+----------
         1 |        39         16 |        55 
           |     70.91      29.09 |    100.00 
           |      3.16       3.02 |      3.11 
-----------+----------------------+----------
     Total |     1,236        530 |     1,766 
           |     69.99      30.01 |    100.00 
           |    100.00     100.00 |    100.00 

          Pearson chi2(1) =   0.0229   Pr = 0.880
           Fisher's exact =                 1.000
   1-sided Fisher's exact =                 0.507

. tab corcat prepos, row col chi exact

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

            |  Pré - Pós COVID-19
     corcat | Pré COVID  Pós COVID |     Total
------------+----------------------+----------
 Não branca |       321        150 |       471 
            |     68.15      31.85 |    100.00 
            |     25.97      28.30 |     26.67 
------------+----------------------+----------
     Branca |       915        380 |     1,295 
            |     70.66      29.34 |    100.00 
            |     74.03      71.70 |     73.33 
------------+----------------------+----------
      Total |     1,236        530 |     1,766 
            |     69.99      30.01 |    100.00 
            |    100.00     100.00 |    100.00 

          Pearson chi2(1) =   1.0306   Pr = 0.310
           Fisher's exact =                 0.319
   1-sided Fisher's exact =                 0.169

. tab dcid1 prepos, row col chi exact

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

           |  Pré - Pós COVID-19
     CID-1 | Pré COVID  Pós COVID |     Total
-----------+----------------------+----------
       Não |     1,140        486 |     1,626 
           |     70.11      29.89 |    100.00 
           |     92.23      91.70 |     92.07 
-----------+----------------------+----------
       Sim |        96         44 |       140 
           |     68.57      31.43 |    100.00 
           |      7.77       8.30 |      7.93 
-----------+----------------------+----------
     Total |     1,236        530 |     1,766 
           |     69.99      30.01 |    100.00 
           |    100.00     100.00 |    100.00 

          Pearson chi2(1) =   0.1454   Pr = 0.703
           Fisher's exact =                 0.701
   1-sided Fisher's exact =                 0.384

. tab cid1 prepos, row col chi exact

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

 Diagnósti |
 gos grupo |  Pré - Pós COVID-19
     CID-1 | Pré COVID  Pós COVID |     Total
-----------+----------------------+----------
         1 |        94         44 |       138 
           |     68.12      31.88 |    100.00 
           |     97.92     100.00 |     98.57 
-----------+----------------------+----------
         2 |         2          0 |         2 
           |    100.00       0.00 |    100.00 
           |      2.08       0.00 |      1.43 
-----------+----------------------+----------
     Total |        96         44 |       140 
           |     68.57      31.43 |    100.00 
           |    100.00     100.00 |    100.00 

          Pearson chi2(1) =   0.9300   Pr = 0.335
           Fisher's exact =                 1.000
   1-sided Fisher's exact =                 0.469

. tab prepos

   Pré - Pós |
    COVID-19 |      Freq.     Percent        Cum.
-------------+-----------------------------------
Pré COVID-19 |      1,236       69.99       69.99
Pós COVID-19 |        530       30.01      100.00
-------------+-----------------------------------
       Total |      1,766      100.00

. tab prepos totalcid, row col chi

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

    Pré - Pós |                        Total Diagnóstigos
     COVID-19 |         1          2          3          4          5          6 |     Total
--------------+------------------------------------------------------------------+----------
 Pré COVID-19 |       759        319         83          7          2          0 |     1,170 
              |     64.87      27.26       7.09       0.60       0.17       0.00 |    100.00 
              |     73.55      64.31      78.30      53.85      66.67       0.00 |     70.87 
--------------+------------------------------------------------------------------+----------
 Pós COVID-19 |       273        177         23          6          1          1 |       481 
              |     56.76      36.80       4.78       1.25       0.21       0.21 |    100.00 
              |     26.45      35.69      21.70      46.15      33.33     100.00 |     29.13 
--------------+------------------------------------------------------------------+----------
        Total |     1,032        496        106         13          3          1 |     1,651 
              |     62.51      30.04       6.42       0.79       0.18       0.06 |    100.00 
              |    100.00     100.00     100.00     100.00     100.00     100.00 |    100.00 

          Pearson chi2(5) =  21.0239   Pr = 0.001

. tab cid1 prepos, row col chi exact

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

 Diagnósti |
 gos grupo |  Pré - Pós COVID-19
     CID-1 | Pré COVID  Pós COVID |     Total
-----------+----------------------+----------
         1 |        94         44 |       138 
           |     68.12      31.88 |    100.00 
           |     97.92     100.00 |     98.57 
-----------+----------------------+----------
         2 |         2          0 |         2 
           |    100.00       0.00 |    100.00 
           |      2.08       0.00 |      1.43 
-----------+----------------------+----------
     Total |        96         44 |       140 
           |     68.57      31.43 |    100.00 
           |    100.00     100.00 |    100.00 

          Pearson chi2(1) =   0.9300   Pr = 0.335
           Fisher's exact =                 1.000
   1-sided Fisher's exact =                 0.469

. tab dcid1 prepos, row col chi exact

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

           |  Pré - Pós COVID-19
     CID-1 | Pré COVID  Pós COVID |     Total
-----------+----------------------+----------
       Não |     1,140        486 |     1,626 
           |     70.11      29.89 |    100.00 
           |     92.23      91.70 |     92.07 
-----------+----------------------+----------
       Sim |        96         44 |       140 
           |     68.57      31.43 |    100.00 
           |      7.77       8.30 |      7.93 
-----------+----------------------+----------
     Total |     1,236        530 |     1,766 
           |     69.99      30.01 |    100.00 
           |    100.00     100.00 |    100.00 

          Pearson chi2(1) =   0.1454   Pr = 0.703
           Fisher's exact =                 0.701
   1-sided Fisher's exact =                 0.384

. tab dcid2 prepos, row col chi exact

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

           |  Pré - Pós COVID-19
     CID-2 | Pré COVID  Pós COVID |     Total
-----------+----------------------+----------
       Não |     1,022        435 |     1,457 
           |     70.14      29.86 |    100.00 
           |     82.69      82.08 |     82.50 
-----------+----------------------+----------
       Sim |       214         95 |       309 
           |     69.26      30.74 |    100.00 
           |     17.31      17.92 |     17.50 
-----------+----------------------+----------
     Total |     1,236        530 |     1,766 
           |     69.99      30.01 |    100.00 
           |    100.00     100.00 |    100.00 

          Pearson chi2(1) =   0.0958   Pr = 0.757
           Fisher's exact =                 0.785
   1-sided Fisher's exact =                 0.403

. tab dcid3 prepos, row col chi exact

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

           |  Pré - Pós COVID-19
     CID-3 | Pré COVID  Pós COVID |     Total
-----------+----------------------+----------
       Não |     1,184        508 |     1,692 
           |     69.98      30.02 |    100.00 
           |     95.79      95.85 |     95.81 
-----------+----------------------+----------
       Sim |        52         22 |        74 
           |     70.27      29.73 |    100.00 
           |      4.21       4.15 |      4.19 
-----------+----------------------+----------
     Total |     1,236        530 |     1,766 
           |     69.99      30.01 |    100.00 
           |    100.00     100.00 |    100.00 

          Pearson chi2(1) =   0.0029   Pr = 0.957
           Fisher's exact =                 1.000
   1-sided Fisher's exact =                 0.536

. tab dcid4 prepos, row col chi exact

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

           |  Pré - Pós COVID-19
     CID-4 | Pré COVID  Pós COVID |     Total
-----------+----------------------+----------
       Não |       286         98 |       384 
           |     74.48      25.52 |    100.00 
           |     23.14      18.49 |     21.74 
-----------+----------------------+----------
       Sim |       950        432 |     1,382 
           |     68.74      31.26 |    100.00 
           |     76.86      81.51 |     78.26 
-----------+----------------------+----------
     Total |     1,236        530 |     1,766 
           |     69.99      30.01 |    100.00 
           |    100.00     100.00 |    100.00 

          Pearson chi2(1) =   4.7107   Pr = 0.030
           Fisher's exact =                 0.032
   1-sided Fisher's exact =                 0.017

. tab dcid5 prepos, row col chi exact

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

           |  Pré - Pós COVID-19
     CID-5 | Pré COVID  Pós COVID |     Total
-----------+----------------------+----------
       Não |     1,015        469 |     1,484 
           |     68.40      31.60 |    100.00 
           |     82.12      88.49 |     84.03 
-----------+----------------------+----------
       Sim |       221         61 |       282 
           |     78.37      21.63 |    100.00 
           |     17.88      11.51 |     15.97 
-----------+----------------------+----------
     Total |     1,236        530 |     1,766 
           |     69.99      30.01 |    100.00 
           |    100.00     100.00 |    100.00 

          Pearson chi2(1) =  11.2200   Pr = 0.001
           Fisher's exact =                 0.001
   1-sided Fisher's exact =                 0.000

. ranksum idade, by(prepos)

Two-sample Wilcoxon rank-sum (Mann-Whitney) test

      prepos |      obs    rank sum    expected
-------------+---------------------------------
 Pré COVID-1 |     1236     1121292     1092006
 Pós COVID-1 |      530      438969      468255
-------------+---------------------------------
    combined |     1766     1560261     1560261

unadjusted variance    96460530
adjustment for ties  -139895.86
                     ----------
adjusted variance      96320634

Ho: idade(prepos==Pré COVID-19) = idade(prepos==Pós COVID-19)
             z =   2.984
    Prob > |z| =   0.0028

. ttest idade, by(prepos)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
Pré COVI |   1,236    54.20388    .2888743     10.1559    53.63714    54.77062
Pós COVI |     530    53.01698    .4121027    9.487316    52.20742    53.82654
---------+--------------------------------------------------------------------
combined |   1,766    53.84768    .2372975    9.972143    53.38226    54.31309
---------+--------------------------------------------------------------------
    diff |            1.186902     .517145                .1726209    2.201184
------------------------------------------------------------------------------
    diff = mean(Pré COVI) - mean(Pós COVI)                        t =   2.2951
Ho: diff = 0                                     degrees of freedom =     1764

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.9891         Pr(|T| > |t|) = 0.0218          Pr(T > t) = 0.0109

. swilk idade

                   Shapiro-Wilk W test for normal data

    Variable |        Obs       W           V         z       Prob>z
-------------+------------------------------------------------------
       idade |      1,766    0.96871     33.141     8.864    0.00000

. ranksum peso, by(prepos)

Two-sample Wilcoxon rank-sum (Mann-Whitney) test

      prepos |      obs    rank sum    expected
-------------+---------------------------------
 Pré COVID-1 |      251     41026.5       41164
 Pós COVID-1 |       76     12601.5       12464
-------------+---------------------------------
    combined |      327       53628       53628

unadjusted variance   521410.67
adjustment for ties      -45.54
                     ----------
adjusted variance     521365.12

Ho: peso(prepos==Pré COVID-19) = peso(prepos==Pós COVID-19)
             z =  -0.190
    Prob > |z| =   0.8490

. ranksum altura, by(prepos)

Two-sample Wilcoxon rank-sum (Mann-Whitney) test

      prepos |      obs    rank sum    expected
-------------+---------------------------------
 Pré COVID-1 |      241       37385       38078
 Pós COVID-1 |       74       12385       11692
-------------+---------------------------------
    combined |      315       49770       49770

unadjusted variance   469628.67
adjustment for ties    -1240.95
                     ----------
adjusted variance     468387.72

Ho: altura(prepos==Pré COVID-19) = altura(prepos==Pós COVID-19)
             z =  -1.013
    Prob > |z| =   0.3113

. ranksum imc, by(prepos)

Two-sample Wilcoxon rank-sum (Mann-Whitney) test

      prepos |      obs    rank sum    expected
-------------+---------------------------------
 Pré COVID-1 |      241     38028.5       38078
 Pós COVID-1 |       74     11741.5       11692
-------------+---------------------------------
    combined |      315       49770       49770

unadjusted variance   469628.67
adjustment for ties       -2.07
                     ----------
adjusted variance     469626.59

Ho: imc(prepos==Pré COVID-19) = imc(prepos==Pós COVID-19)
             z =  -0.072
    Prob > |z| =   0.9424

. ranksum pas, by(prepos)

Two-sample Wilcoxon rank-sum (Mann-Whitney) test

      prepos |      obs    rank sum    expected
-------------+---------------------------------
 Pré COVID-1 |      234     33334.5       34866
 Pós COVID-1 |       63     10918.5        9387
-------------+---------------------------------
    combined |      297       44253       44253

unadjusted variance   366093.00
adjustment for ties     -538.20
                     ----------
adjusted variance     365554.80

Ho: pas(prepos==Pré COVID-19) = pas(prepos==Pós COVID-19)
             z =  -2.533
    Prob > |z| =   0.0113

. ranksum pad, by(prepos)

Two-sample Wilcoxon rank-sum (Mann-Whitney) test

      prepos |      obs    rank sum    expected
-------------+---------------------------------
 Pré COVID-1 |      200     24116.5       25800
 Pós COVID-1 |       57      9036.5        7353
-------------+---------------------------------
    combined |      257       33153       33153

unadjusted variance   245100.00
adjustment for ties     -515.06
                     ----------
adjusted variance     244584.94

Ho: pad(prepos==Pré COVID-19) = pad(prepos==Pós COVID-19)
             z =  -3.404
    Prob > |z| =   0.0007

. ranksum idademenopausa , by(prepos)

Two-sample Wilcoxon rank-sum (Mann-Whitney) test

      prepos |      obs    rank sum    expected
-------------+---------------------------------
 Pré COVID-1 |      502      185214      186493
 Pós COVID-1 |      240       90439       89160
-------------+---------------------------------
    combined |      742      275653      275653

unadjusted variance  7459720.00
adjustment for ties   -28746.52
                     ----------
adjusted variance    7430973.48

Ho: idadem~a(prepos==Pré COVID-19) = idadem~a(prepos==Pós COVID-19)
             z =  -0.469
    Prob > |z| =   0.6389

. ranksum paridade , by(prepos)

Two-sample Wilcoxon rank-sum (Mann-Whitney) test

      prepos |      obs    rank sum    expected
-------------+---------------------------------
 Pré COVID-1 |      309     78712.5     80803.5
 Pós COVID-1 |      213     57790.5     55699.5
-------------+---------------------------------
    combined |      522      136503      136503

unadjusted variance  2868524.25
adjustment for ties  -146638.14
                     ----------
adjusted variance    2721886.11

Ho: paridade(prepos==Pré COVID-19) = paridade(prepos==Pós COVID-19)
             z =  -1.267
    Prob > |z| =   0.2050

. ranksum menarca , by(prepos)

Two-sample Wilcoxon rank-sum (Mann-Whitney) test

      prepos |      obs    rank sum    expected
-------------+---------------------------------
 Pré COVID-1 |      176     26392.5       26840
 Pós COVID-1 |      128     19967.5       19520
-------------+---------------------------------
    combined |      304       46360       46360

unadjusted variance   572586.67
adjustment for ties   -14761.87
                     ----------
adjusted variance     557824.80

Ho: menarca(prepos==Pré COVID-19) = menarca(prepos==Pós COVID-19)
             z =  -0.599
    Prob > |z| =   0.5491

. ranksum coitarca , by(prepos)

Two-sample Wilcoxon rank-sum (Mann-Whitney) test

      prepos |      obs    rank sum    expected
-------------+---------------------------------
 Pré COVID-1 |       21       648.5       661.5
 Pós COVID-1 |       41      1304.5      1291.5
-------------+---------------------------------
    combined |       62        1953        1953

unadjusted variance     4520.25
adjustment for ties      -72.28
                     ----------
adjusted variance       4447.97

Ho: coitarca(prepos==Pré COVID-19) = coitarca(prepos==Pós COVID-19)
             z =  -0.195
    Prob > |z| =   0.8455

. bysort prepos: tab dcid1 if menop==1

---------------------------------------------------------------------------------------------------------
-> prepos = Pré COVID-19

      CID-1 |      Freq.     Percent        Cum.
------------+-----------------------------------
        Não |        522       89.08       89.08
        Sim |         64       10.92      100.00
------------+-----------------------------------
      Total |        586      100.00

---------------------------------------------------------------------------------------------------------
-> prepos = Pós COVID-19

      CID-1 |      Freq.     Percent        Cum.
------------+-----------------------------------
        Não |        242       90.98       90.98
        Sim |         24        9.02      100.00
------------+-----------------------------------
      Total |        266      100.00


. 
. 
. 
. bysort prepos: tab dcid1 if menop==1

---------------------------------------------------------------------------------------------------------
-> prepos = Pré COVID-19

      CID-1 |      Freq.     Percent        Cum.
------------+-----------------------------------
        Não |        522       89.08       89.08
        Sim |         64       10.92      100.00
------------+-----------------------------------
      Total |        586      100.00

---------------------------------------------------------------------------------------------------------
-> prepos = Pós COVID-19

      CID-1 |      Freq.     Percent        Cum.
------------+-----------------------------------
        Não |        242       90.98       90.98
        Sim |         24        9.02      100.00
------------+-----------------------------------
      Total |        266      100.00


. 
. 
. 
. bysort prepos: tab dcid1 if menop==1

---------------------------------------------------------------------------------------------------------
-> prepos = Pré COVID-19

      CID-1 |      Freq.     Percent        Cum.
------------+-----------------------------------
        Não |        522       89.08       89.08
        Sim |         64       10.92      100.00
------------+-----------------------------------
      Total |        586      100.00

---------------------------------------------------------------------------------------------------------
-> prepos = Pós COVID-19

      CID-1 |      Freq.     Percent        Cum.
------------+-----------------------------------
        Não |        242       90.98       90.98
        Sim |         24        9.02      100.00
------------+-----------------------------------
      Total |        266      100.00


. 
. 
. 
. bysort prepos: tab dcid1 if menop==1

---------------------------------------------------------------------------------------------------------
-> prepos = Pré COVID-19

      CID-1 |      Freq.     Percent        Cum.
------------+-----------------------------------
        Não |        522       89.08       89.08
        Sim |         64       10.92      100.00
------------+-----------------------------------
      Total |        586      100.00

---------------------------------------------------------------------------------------------------------
-> prepos = Pós COVID-19

      CID-1 |      Freq.     Percent        Cum.
------------+-----------------------------------
        Não |        242       90.98       90.98
        Sim |         24        9.02      100.00
------------+-----------------------------------
      Total |        266      100.00


. bysort prepos: tab dcid1 if menop==1, row col chi

---------------------------------------------------------------------------------------------------------
-> prepos = Pré COVID-19
option row not allowed
r(198);

. tab prepos dcid1 if menop==1, row col chi

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

    Pré - Pós |         CID-1
     COVID-19 |       Não        Sim |     Total
--------------+----------------------+----------
 Pré COVID-19 |       522         64 |       586 
              |     89.08      10.92 |    100.00 
              |     68.32      72.73 |     68.78 
--------------+----------------------+----------
 Pós COVID-19 |       242         24 |       266 
              |     90.98       9.02 |    100.00 
              |     31.68      27.27 |     31.22 
--------------+----------------------+----------
        Total |       764         88 |       852 
              |     89.67      10.33 |    100.00 
              |    100.00     100.00 |    100.00 

          Pearson chi2(1) =   0.7123   Pr = 0.399

. bysort prepos: tab menop if dcid1==1

---------------------------------------------------------------------------------------------------------
-> prepos = Pré COVID-19

  Menopausa |      Freq.     Percent        Cum.
------------+-----------------------------------
        Não |         32       33.33       33.33
        Sim |         64       66.67      100.00
------------+-----------------------------------
      Total |         96      100.00

---------------------------------------------------------------------------------------------------------
-> prepos = Pós COVID-19

  Menopausa |      Freq.     Percent        Cum.
------------+-----------------------------------
        Não |         20       45.45       45.45
        Sim |         24       54.55      100.00
------------+-----------------------------------
      Total |         44      100.00


. bysort prepos: tab dcid1 if menop==1

---------------------------------------------------------------------------------------------------------
-> prepos = Pré COVID-19

      CID-1 |      Freq.     Percent        Cum.
------------+-----------------------------------
        Não |        522       89.08       89.08
        Sim |         64       10.92      100.00
------------+-----------------------------------
      Total |        586      100.00

---------------------------------------------------------------------------------------------------------
-> prepos = Pós COVID-19

      CID-1 |      Freq.     Percent        Cum.
------------+-----------------------------------
        Não |        242       90.98       90.98
        Sim |         24        9.02      100.00
------------+-----------------------------------
      Total |        266      100.00


. bysort prepos: tab dcid2 if menop==1

---------------------------------------------------------------------------------------------------------
-> prepos = Pré COVID-19

      CID-2 |      Freq.     Percent        Cum.
------------+-----------------------------------
        Não |        498       84.98       84.98
        Sim |         88       15.02      100.00
------------+-----------------------------------
      Total |        586      100.00

---------------------------------------------------------------------------------------------------------
-> prepos = Pós COVID-19

      CID-2 |      Freq.     Percent        Cum.
------------+-----------------------------------
        Não |        218       81.95       81.95
        Sim |         48       18.05      100.00
------------+-----------------------------------
      Total |        266      100.00


. bysort prepos: tab dcid1 dcid2 if menop==1

---------------------------------------------------------------------------------------------------------
-> prepos = Pré COVID-19

           |         CID-2
     CID-1 |       Não        Sim |     Total
-----------+----------------------+----------
       Não |       449         73 |       522 
       Sim |        49         15 |        64 
-----------+----------------------+----------
     Total |       498         88 |       586 


---------------------------------------------------------------------------------------------------------
-> prepos = Pós COVID-19

           |         CID-2
     CID-1 |       Não        Sim |     Total
-----------+----------------------+----------
       Não |       195         47 |       242 
       Sim |        23          1 |        24 
-----------+----------------------+----------
     Total |       218         48 |       266 



. bysort prepos: tab menopausa dcid1 dcid2

---------------------------------------------------------------------------------------------------------
-> prepos = Pré COVID-19
too many variables specified
r(103);

. bysort prepos: tab dcid1 if menop==1

---------------------------------------------------------------------------------------------------------
-> prepos = Pré COVID-19

      CID-1 |      Freq.     Percent        Cum.
------------+-----------------------------------
        Não |        522       89.08       89.08
        Sim |         64       10.92      100.00
------------+-----------------------------------
      Total |        586      100.00

---------------------------------------------------------------------------------------------------------
-> prepos = Pós COVID-19

      CID-1 |      Freq.     Percent        Cum.
------------+-----------------------------------
        Não |        242       90.98       90.98
        Sim |         24        9.02      100.00
------------+-----------------------------------
      Total |        266      100.00


. tab prepos dcid1 if menop==1, row col chi exact

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

    Pré - Pós |         CID-1
     COVID-19 |       Não        Sim |     Total
--------------+----------------------+----------
 Pré COVID-19 |       522         64 |       586 
              |     89.08      10.92 |    100.00 
              |     68.32      72.73 |     68.78 
--------------+----------------------+----------
 Pós COVID-19 |       242         24 |       266 
              |     90.98       9.02 |    100.00 
              |     31.68      27.27 |     31.22 
--------------+----------------------+----------
        Total |       764         88 |       852 
              |     89.67      10.33 |    100.00 
              |    100.00     100.00 |    100.00 

          Pearson chi2(1) =   0.7123   Pr = 0.399
           Fisher's exact =                 0.466
   1-sided Fisher's exact =                 0.237

. tab prepos dcid2 if menop==1, row col chi exact

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

    Pré - Pós |         CID-2
     COVID-19 |       Não        Sim |     Total
--------------+----------------------+----------
 Pré COVID-19 |       498         88 |       586 
              |     84.98      15.02 |    100.00 
              |     69.55      64.71 |     68.78 
--------------+----------------------+----------
 Pós COVID-19 |       218         48 |       266 
              |     81.95      18.05 |    100.00 
              |     30.45      35.29 |     31.22 
--------------+----------------------+----------
        Total |       716        136 |       852 
              |     84.04      15.96 |    100.00 
              |    100.00     100.00 |    100.00 

          Pearson chi2(1) =   1.2505   Pr = 0.263
           Fisher's exact =                 0.268
   1-sided Fisher's exact =                 0.155

. tab prepos dcid3 if menop==1, row col chi exact

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

    Pré - Pós |         CID-3
     COVID-19 |       Não        Sim |     Total
--------------+----------------------+----------
 Pré COVID-19 |       571         15 |       586 
              |     97.44       2.56 |    100.00 
              |     69.21      55.56 |     68.78 
--------------+----------------------+----------
 Pós COVID-19 |       254         12 |       266 
              |     95.49       4.51 |    100.00 
              |     30.79      44.44 |     31.22 
--------------+----------------------+----------
        Total |       825         27 |       852 
              |     96.83       3.17 |    100.00 
              |    100.00     100.00 |    100.00 

          Pearson chi2(1) =   2.2707   Pr = 0.132
           Fisher's exact =                 0.142
   1-sided Fisher's exact =                 0.100

. tab prepos dcid4 if menop==1, row col chi exact

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

    Pré - Pós |         CID-4
     COVID-19 |       Não        Sim |     Total
--------------+----------------------+----------
 Pré COVID-19 |        86        500 |       586 
              |     14.68      85.32 |    100.00 
              |     74.14      67.93 |     68.78 
--------------+----------------------+----------
 Pós COVID-19 |        30        236 |       266 
              |     11.28      88.72 |    100.00 
              |     25.86      32.07 |     31.22 
--------------+----------------------+----------
        Total |       116        736 |       852 
              |     13.62      86.38 |    100.00 
              |    100.00     100.00 |    100.00 

          Pearson chi2(1) =   1.7956   Pr = 0.180
           Fisher's exact =                 0.197
   1-sided Fisher's exact =                 0.108

. tab prepos dcid5 if menop==1, row col chi exact

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

    Pré - Pós |         CID-5
     COVID-19 |       Não        Sim |     Total
--------------+----------------------+----------
 Pré COVID-19 |       505         81 |       586 
              |     86.18      13.82 |    100.00 
              |     67.69      76.42 |     68.78 
--------------+----------------------+----------
 Pós COVID-19 |       241         25 |       266 
              |     90.60       9.40 |    100.00 
              |     32.31      23.58 |     31.22 
--------------+----------------------+----------
        Total |       746        106 |       852 
              |     87.56      12.44 |    100.00 
              |    100.00     100.00 |    100.00 

          Pearson chi2(1) =   3.2871   Pr = 0.070
           Fisher's exact =                 0.074
   1-sided Fisher's exact =                 0.042

. logistic prepos endometr

Logistic regression                             Number of obs     =      1,766
                                                LR chi2(1)        =       7.88
                                                Prob > chi2       =     0.0050
Log likelihood = -1075.0162                     Pseudo R2         =     0.0037

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    endometr |   1.735119   .3347776     2.86   0.004     1.188767    2.532573
       _cons |   .4116638   .0223248   -16.37   0.000     .3701532    .4578297
------------------------------------------------------------------------------

. logistic prepos dlp

Logistic regression                             Number of obs     =      1,766
                                                LR chi2(1)        =       0.33
                                                Prob > chi2       =     0.5662
Log likelihood = -1078.7912                     Pseudo R2         =     0.0002

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         dlp |   1.083971    .151825     0.58   0.565     .8237491    1.426396
       _cons |   .4232246    .024043   -15.14   0.000     .3786299    .4730715
------------------------------------------------------------------------------

. logistic prepos osteop

Logistic regression                             Number of obs     =      1,766
                                                LR chi2(1)        =       0.48
                                                Prob > chi2       =     0.4875
Log likelihood = -1078.7148                     Pseudo R2         =     0.0002

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      osteop |   1.093742   .1407067     0.70   0.486     .8499836    1.407407
       _cons |   .4211055   .0245243   -14.85   0.000     .3756805     .472023
------------------------------------------------------------------------------

. logistic prepos hipo

Logistic regression                             Number of obs     =      1,766
                                                LR chi2(1)        =       2.10
                                                Prob > chi2       =     0.1478
Log likelihood = -1077.9081                     Pseudo R2         =     0.0010

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        hipo |   1.263063   .2017964     1.46   0.144     .9234863    1.727505
       _cons |   .4173442   .0231159   -15.78   0.000     .3744104    .4652012
------------------------------------------------------------------------------

. logistic prepos obesidade

Logistic regression                             Number of obs     =      1,766
                                                LR chi2(1)        =       1.51
                                                Prob > chi2       =     0.2198
Log likelihood = -1078.2028                     Pseudo R2         =     0.0007

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   obesidade |   .4974234    .277955    -1.25   0.211     .1663733    1.487198
       _cons |   .8571429   .4768703    -0.28   0.782     .2880636    2.550457
------------------------------------------------------------------------------

. logistic prepos fogacho

Logistic regression                             Number of obs     =      1,766
                                                LR chi2(1)        =       7.72
                                                Prob > chi2       =     0.0055
Log likelihood = -1075.0949                     Pseudo R2         =     0.0036

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     fogacho |   1.344038   .1426161     2.79   0.005     1.091667    1.654751
       _cons |   .3822055   .0257296   -14.29   0.000     .3349616    .4361128
------------------------------------------------------------------------------

. logistic prepos sangr

Logistic regression                             Number of obs     =      1,766
                                                LR chi2(1)        =      19.92
                                                Prob > chi2       =     0.0000
Log likelihood = -1068.9976                     Pseudo R2         =     0.0092

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       sangr |   1.731455   .2105654     4.51   0.000     1.364254    2.197493
       _cons |   .3782178    .022718   -16.19   0.000     .3362125    .4254711
------------------------------------------------------------------------------

. logistic prepos diu

Logistic regression                             Number of obs     =      1,766
                                                LR chi2(1)        =       3.62
                                                Prob > chi2       =     0.0570
Log likelihood = -1077.1452                     Pseudo R2         =     0.0017

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         diu |   1.625692   .4078297     1.94   0.053     .9942634    2.658124
       _cons |   .4200837    .022343   -16.31   0.000     .3784975     .466239
------------------------------------------------------------------------------

. logistic prepos dm

Logistic regression                             Number of obs     =      1,766
                                                LR chi2(1)        =       0.08
                                                Prob > chi2       =     0.7823
Log likelihood = -1078.9176                     Pseudo R2         =     0.0000

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          dm |    1.04386   .1618442     0.28   0.782     .7703153    1.414542
       _cons |    .426457   .0237222   -15.32   0.000     .3824073    .4755808
------------------------------------------------------------------------------

. logistic prepos tabag

Logistic regression                             Number of obs     =      1,766
                                                LR chi2(1)        =       0.09
                                                Prob > chi2       =     0.7605
Log likelihood = -1078.9093                     Pseudo R2         =     0.0000

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       tabag |   1.074391   .2518544     0.31   0.760     .6786209    1.700974
       _cons |    .427234   .0227804   -15.95   0.000     .3848391    .4742993
------------------------------------------------------------------------------

. logistic prepos cardiop

Logistic regression                             Number of obs     =      1,766
                                                LR chi2(1)        =       1.38
                                                Prob > chi2       =     0.2398
Log likelihood = -1078.2649                     Pseudo R2         =     0.0006

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     cardiop |   .7018874   .2174107    -1.14   0.253      .382478    1.288037
       _cons |   .4336134   .0228557   -15.85   0.000     .3910534    .4808055
------------------------------------------------------------------------------

. logistic prepos etilism

Logistic regression                             Number of obs     =      1,766
                                                LR chi2(1)        =       4.61
                                                Prob > chi2       =     0.0318
Log likelihood =   -1076.65                     Pseudo R2         =     0.0021

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     etilism |   2.598459   1.143474     2.17   0.030     1.096825    6.155939
       _cons |   .4233279    .022169   -16.41   0.000      .382033    .4690865
------------------------------------------------------------------------------

. logistic prepos corcat

Logistic regression                             Number of obs     =      1,766
                                                LR chi2(1)        =       1.02
                                                Prob > chi2       =     0.3117
Log likelihood = -1078.4441                     Pseudo R2         =     0.0005

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      corcat |   .8887429   .1032871    -1.01   0.310     .7077046    1.116093
       _cons |   .4672899   .0462167    -7.69   0.000     .3849457    .5672484
------------------------------------------------------------------------------

. logistic prepos multimorbcat

Logistic regression                             Number of obs     =      1,766
                                                LR chi2(1)        =       0.02
                                                Prob > chi2       =     0.8794
Log likelihood = -1078.9442                     Pseudo R2         =     0.0000

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
multimorbcat |   .9554026   .2880851    -0.15   0.880       .52908    1.725248
       _cons |   .4294069   .0226446   -16.03   0.000     .3872408    .4761643
------------------------------------------------------------------------------

. gen idadecat = idade

. recode idadecat min/50=0 50.01/max=1
(idadecat: 1766 changes made)

. label define idade 0(Até 50 anos) 1(Mais que 50 anos)
invalid syntax
r(198);

. label define idade 0"Até 50 anos" 1"Mais que 50 anos"

. label val idadecat idade

. tab idadecat

        idadecat |      Freq.     Percent        Cum.
-----------------+-----------------------------------
     Até 50 anos |        555       31.43       31.43
Mais que 50 anos |      1,211       68.57      100.00
-----------------+-----------------------------------
           Total |      1,766      100.00

. logistic prepos idadecat

Logistic regression                             Number of obs     =      1,766
                                                LR chi2(1)        =       2.24
                                                Prob > chi2       =     0.1344
Log likelihood = -1077.8351                     Pseudo R2         =     0.0010

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    idadecat |   .8468836   .0936977    -1.50   0.133     .6817864     1.05196
       _cons |   .4799999   .0435247    -8.09   0.000     .4018438     .573357
------------------------------------------------------------------------------

. logistic prepos dcid1

Logistic regression                             Number of obs     =      1,766
                                                LR chi2(1)        =       0.14
                                                Prob > chi2       =     0.7041
Log likelihood = -1078.8836                     Pseudo R2         =     0.0001

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       dcid1 |   1.075103   .2042094     0.38   0.703     .7409168    1.560022
       _cons |   .4263158   .0230952   -15.74   0.000     .3833704     .474072
------------------------------------------------------------------------------

. logistic prepos cid1
note: cid1 != 1 predicts failure perfectly
      cid1 dropped and 2 obs not used


Logistic regression                             Number of obs     =        138
                                                LR chi2(0)        =       0.00
                                                Prob > chi2       =          .
Log likelihood = -86.386955                     Pseudo R2         =     0.0000

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        cid1 |          1  (omitted)
       _cons |   .4680851   .0855016    -4.16   0.000     .3272218    .6695876
------------------------------------------------------------------------------

. logistic prepos i.cid1
note: 1.cid1 != 1 predicts failure perfectly
      1.cid1 dropped and 2 obs not used

note: 2.cid1 omitted because of collinearity

Logistic regression                             Number of obs     =        138
                                                LR chi2(0)        =       0.00
                                                Prob > chi2       =          .
Log likelihood = -86.386955                     Pseudo R2         =     0.0000

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      2.cid1 |          1  (empty)
       _cons |   .4680851   .0855016    -4.16   0.000     .3272218    .6695876
------------------------------------------------------------------------------

. logistic prepos dcid2

Logistic regression                             Number of obs     =      1,766
                                                LR chi2(1)        =       0.10
                                                Prob > chi2       =     0.7573
Log likelihood =  -1078.908                     Pseudo R2         =     0.0000

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       dcid2 |   1.042969   .1417692     0.31   0.757     .7990409    1.361363
       _cons |    .425636   .0243668   -14.92   0.000     .3804599    .4761764
------------------------------------------------------------------------------

. logistic prepos dcid3

Logistic regression                             Number of obs     =      1,766
                                                LR chi2(1)        =       0.00
                                                Prob > chi2       =     0.9569
Log likelihood = -1078.9543                     Pseudo R2         =     0.0000

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       dcid3 |    .986069   .2561852    -0.05   0.957     .5925995    1.640791
       _cons |   .4290541   .0227564   -15.95   0.000     .3866922    .4760566
------------------------------------------------------------------------------

. logistic prepos cid4

Logistic regression                             Number of obs     =      1,382
                                                LR chi2(1)        =       0.84
                                                Prob > chi2       =     0.3598
Log likelihood =  -858.0206                     Pseudo R2         =     0.0005

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        cid4 |   1.132893   .1532366     0.92   0.356     .8690693    1.476807
       _cons |   .3931299   .0662914    -5.54   0.000     .2824897    .5471036
------------------------------------------------------------------------------

. logistic prepos dcid4

Logistic regression                             Number of obs     =      1,766
                                                LR chi2(1)        =       4.82
                                                Prob > chi2       =     0.0281
Log likelihood = -1076.5462                     Pseudo R2         =     0.0022

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       dcid4 |   1.327089   .1733771     2.17   0.030     1.027295    1.714372
       _cons |   .3426573   .0401079    -9.15   0.000     .2724127    .4310154
------------------------------------------------------------------------------

. logistic prepos dcid5

Logistic regression                             Number of obs     =      1,766
                                                LR chi2(1)        =      11.81
                                                Prob > chi2       =     0.0006
Log likelihood =  -1073.049                     Pseudo R2         =     0.0055

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       dcid5 |   .5973526   .0926104    -3.32   0.001     .4408238    .8094621
       _cons |    .462069   .0257991   -13.83   0.000     .4141722    .5155047
------------------------------------------------------------------------------

. logistic prepos i.cid5
note: 1.cid5 != 1 predicts failure perfectly
      1.cid5 dropped and 1 obs not used

note: 2.cid5 omitted because of collinearity

Logistic regression                             Number of obs     =        281
                                                LR chi2(0)        =       0.00
                                                Prob > chi2       =          .
Log likelihood =  -147.0163                     Pseudo R2         =     0.0000

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      2.cid5 |          1  (empty)
       _cons |   .2772727   .0401221    -8.86   0.000     .2088026    .3681954
------------------------------------------------------------------------------

. logistic prepos i.cid4
note: 4.cid4 != 0 predicts failure perfectly
      4.cid4 dropped and 1 obs not used

note: 5.cid4 != 0 predicts success perfectly
      5.cid4 dropped and 1 obs not used


Logistic regression                             Number of obs     =      1,380
                                                LR chi2(2)        =       0.64
                                                Prob > chi2       =     0.7260
Log likelihood = -856.58172                     Pseudo R2         =     0.0004

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        cid4 |
          2  |   1.071055   .1825385     0.40   0.687     .7669065    1.495825
          3  |   1.420922    .692811     0.72   0.471     .5464399    3.694858
          4  |          1  (empty)
          5  |          1  (empty)
             |
       _cons |   .4478528   .0282066   -12.75   0.000     .3958448    .5066937
------------------------------------------------------------------------------

. logistic prepos i.cid3
note: 1.cid3 omitted because of collinearity

Logistic regression                             Number of obs     =         74
                                                LR chi2(0)        =       0.00
                                                Prob > chi2       =          .
Log likelihood =  -45.03321                     Pseudo R2         =     0.0000

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      1.cid3 |          1  (omitted)
       _cons |   .4230769   .1076025    -3.38   0.001     .2569985     .696479
------------------------------------------------------------------------------

. logistic prepos sangr endometr

Logistic regression                             Number of obs     =      1,766
                                                LR chi2(2)        =      27.25
                                                Prob > chi2       =     0.0000
Log likelihood = -1065.3311                     Pseudo R2         =     0.0126

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       sangr |   1.720691   .2097998     4.45   0.000     1.354933    2.185183
    endometr |   1.707153   .3316568     2.75   0.006     1.166555     2.49827
       _cons |   .3640698   .0225811   -16.29   0.000     .3223959    .4111306
------------------------------------------------------------------------------

. logistic prepos sangr endometr fogacho

Logistic regression                             Number of obs     =      1,766
                                                LR chi2(3)        =      30.30
                                                Prob > chi2       =     0.0000
Log likelihood = -1063.8078                     Pseudo R2         =     0.0140

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       sangr |   1.646065   .2049896     4.00   0.000     1.289568    2.101114
    endometr |   1.669579   .3250167     2.63   0.008     1.139997    2.445177
     fogacho |   1.210825   .1323619     1.75   0.080     .9773089    1.500137
       _cons |   .3419752   .0246832   -14.87   0.000     .2968632    .3939425
------------------------------------------------------------------------------

. logistic prepos sangr endometr etilism

Logistic regression                             Number of obs     =      1,766
                                                LR chi2(3)        =      32.17
                                                Prob > chi2       =     0.0000
Log likelihood = -1062.8716                     Pseudo R2         =     0.0149

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       sangr |    1.73252   .2115679     4.50   0.000     1.363745    2.201017
    endometr |   1.700316   .3308656     2.73   0.006     1.161165    2.489806
     etilism |   2.701727   1.197219     2.24   0.025     1.133568    6.439249
       _cons |   .3587463   .0224339   -16.39   0.000     .3173645     .405524
------------------------------------------------------------------------------

. logistic prepos sangr endometr etilism diu

Logistic regression                             Number of obs     =      1,766
                                                LR chi2(4)        =      34.03
                                                Prob > chi2       =     0.0000
Log likelihood = -1061.9413                     Pseudo R2         =     0.0158

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       sangr |   1.702743   .2092583     4.33   0.000     1.338262    2.166494
    endometr |   1.689369    .329429     2.69   0.007     1.152761    2.475768
     etilism |   2.696111   1.196931     2.23   0.025     1.129404    6.436154
         diu |   1.424859    .365402     1.38   0.167     .8619518    2.355378
       _cons |   .3550745   .0223907   -16.42   0.000      .313793    .4017868
------------------------------------------------------------------------------

. logistic prepos sangr dcid5 endometr etilism

Logistic regression                             Number of obs     =      1,766
                                                LR chi2(4)        =      41.85
                                                Prob > chi2       =     0.0000
Log likelihood = -1058.0322                     Pseudo R2         =     0.0194

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       sangr |   1.700581   .2083286     4.33   0.000     1.337585    2.162087
       dcid5 |   .6234639   .0975447    -3.02   0.003     .4588129    .8472019
    endometr |   1.651465   .3223444     2.57   0.010     1.126486      2.4211
     etilism |   2.757594   1.226366     2.28   0.023     1.153403    6.592945
       _cons |   .3865354   .0256971   -14.30   0.000     .3393133    .4403293
------------------------------------------------------------------------------

. logistic prepos sangr dcid5 dcid4 endometr etilism

Logistic regression                             Number of obs     =      1,766
                                                LR chi2(5)        =      42.00
                                                Prob > chi2       =     0.0000
Log likelihood = -1057.9558                     Pseudo R2         =     0.0195

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       sangr |   1.712285   .2119642     4.34   0.000       1.3434    2.182461
       dcid5 |   .6031175   .1074123    -2.84   0.005     .4254094    .8550605
       dcid4 |   .9418876   .1440752    -0.39   0.696     .6979039    1.271167
    endometr |   1.666883   .3279196     2.60   0.009     1.133578    2.451087
     etilism |   2.773945    1.23448     2.29   0.022     1.159553    6.635983
       _cons |   .4063246   .0583556    -6.27   0.000     .3066374      .53842
------------------------------------------------------------------------------

. logistic prepos sangr dcid5  endometr etilism dcid4

Logistic regression                             Number of obs     =      1,766
                                                LR chi2(5)        =      42.00
                                                Prob > chi2       =     0.0000
Log likelihood = -1057.9558                     Pseudo R2         =     0.0195

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       sangr |   1.712285   .2119642     4.34   0.000       1.3434    2.182461
       dcid5 |   .6031175   .1074123    -2.84   0.005     .4254094    .8550605
    endometr |   1.666883   .3279196     2.60   0.009     1.133578    2.451087
     etilism |   2.773945    1.23448     2.29   0.022     1.159553    6.635983
       dcid4 |   .9418876   .1440752    -0.39   0.696     .6979039    1.271167
       _cons |   .4063246   .0583556    -6.27   0.000     .3066374      .53842
------------------------------------------------------------------------------

. logistic prepos sangr dcid5 dcid4

Logistic regression                             Number of obs     =      1,766
                                                LR chi2(3)        =      30.22
                                                Prob > chi2       =     0.0000
Log likelihood = -1063.8481                     Pseudo R2         =     0.0140

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       sangr |   1.698435   .2094147     4.30   0.000      1.33382    2.162724
       dcid5 |   .6146461   .1091116    -2.74   0.006     .4340306     .870422
       dcid4 |    .996523   .1510071    -0.02   0.982     .7404596    1.341138
       _cons |   .4087918    .058649    -6.24   0.000     .3085894     .541531
------------------------------------------------------------------------------

. logistic prepos sangr dcid5  endometr etilism

Logistic regression                             Number of obs     =      1,766
                                                LR chi2(4)        =      41.85
                                                Prob > chi2       =     0.0000
Log likelihood = -1058.0322                     Pseudo R2         =     0.0194

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       sangr |   1.700581   .2083286     4.33   0.000     1.337585    2.162087
       dcid5 |   .6234639   .0975447    -3.02   0.003     .4588129    .8472019
    endometr |   1.651465   .3223444     2.57   0.010     1.126486      2.4211
     etilism |   2.757594   1.226366     2.28   0.023     1.153403    6.592945
       _cons |   .3865354   .0256971   -14.30   0.000     .3393133    .4403293
------------------------------------------------------------------------------

. logistic prepos sangr dcid5  endometr etilism idadecat

Logistic regression                             Number of obs     =      1,766
                                                LR chi2(5)        =      43.56
                                                Prob > chi2       =     0.0000
Log likelihood = -1057.1767                     Pseudo R2         =     0.0202

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       sangr |   1.703577   .2088231     4.35   0.000     1.339745    2.166214
       dcid5 |   .6155364   .0965836    -3.09   0.002     .4525765    .8371736
    endometr |    1.57269   .3124759     2.28   0.023     1.065416    2.321493
     etilism |   2.799332   1.246511     2.31   0.021     1.169563    6.700157
    idadecat |   .8607448   .0983794    -1.31   0.190     .6879958    1.076869
       _cons |   .4300408   .0449147    -8.08   0.000     .3504351    .5277298
------------------------------------------------------------------------------

. logistic prepos idadecat sangr dcid5  endometr etilism

Logistic regression                             Number of obs     =      1,766
                                                LR chi2(5)        =      43.56
                                                Prob > chi2       =     0.0000
Log likelihood = -1057.1767                     Pseudo R2         =     0.0202

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    idadecat |   .8607448   .0983794    -1.31   0.190     .6879958    1.076869
       sangr |   1.703577   .2088231     4.35   0.000     1.339745    2.166214
       dcid5 |   .6155364   .0965836    -3.09   0.002     .4525765    .8371736
    endometr |    1.57269   .3124759     2.28   0.023     1.065416    2.321493
     etilism |   2.799332   1.246511     2.31   0.021     1.169563    6.700157
       _cons |   .4300408   .0449147    -8.08   0.000     .3504351    .5277298
------------------------------------------------------------------------------

. logistic prepos sangr dcid5  endometr etilism

Logistic regression                             Number of obs     =      1,766
                                                LR chi2(4)        =      41.85
                                                Prob > chi2       =     0.0000
Log likelihood = -1058.0322                     Pseudo R2         =     0.0194

------------------------------------------------------------------------------
      prepos | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       sangr |   1.700581   .2083286     4.33   0.000     1.337585    2.162087
       dcid5 |   .6234639   .0975447    -3.02   0.003     .4588129    .8472019
    endometr |   1.651465   .3223444     2.57   0.010     1.126486      2.4211
     etilism |   2.757594   1.226366     2.28   0.023     1.153403    6.592945
       _cons |   .3865354   .0256971   -14.30   0.000     .3393133    .4403293
------------------------------------------------------------------------------

. save "C:\Users\User\Dropbox\Ginecologia e Obstetrícia\Isabel - Atenção primária a saúde\atendimentos.dt
> a", replace
file C:\Users\User\Dropbox\Ginecologia e Obstetrícia\Isabel - Atenção primária a saúde\atendimentos.dta s
> aved

. exit, clear
